humanism and problem solving in the classroom

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What Is Humanistic Learning Theory in Education?

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Good teachers are always looking for ways to improve their methods to help students thrive in their classroom. Different learning theories and techniques help teachers connect with different students based on their learning style and abilities. Teaching strategies that are student-centered often have great success in helping students learn and grow better. Learner-centered approaches place the student as the authority in the educational setting, helping ensure that they are the focus of education and are in control of their learning to an extent. 

The idea of student-centered learning is an example of the humanistic learning theory in action. It’s valuable for current and aspiring educators alike to learn about student-centered education and other humanistic approaches to use in their classroom. These approaches can be vital in helping students truly learn and succeed in their education. Learn more about the humanistic learning theory and discover how it can be implemented in the classroom.   

The humanistic theory in education.

In history humanistic psychology is an outlook or system of thought that focuses on human beings rather than supernatural or divine insight. This system stresses that human beings are inherently good, and that basic needs are vital to human behaviors. Humanistic psychology also focuses on finding rational ways to solve these human problems. At its root, the psychology of humanism focuses on human virtue. It has been an important movement throughout history, from Greek and Latin roots to Renaissance and now modern revivals. 

This theory and approach in education takes root in humanistic psychology, with the key concepts focusing on the idea that children are good at the core and that education should focus on rational ways to teach the “whole” child. This theory states that the student is the authority on how they learn, and that all of their needs should be met in order for them to learn well. For example, a student who is hungry won’t have as much attention to give to learning. So schools offer meals to students so that need is met, and they can focus on education. The humanistic theory approach engages social skills, feelings, intellect, artistic skills, practical skills, and more as part of their education. Self-esteem, goals, and full autonomy are key learning elements in the humanistic learning theory. 

The humanistic learning theory was developed by Abraham Maslow, Carl Rogers, and James F. T. Bugental in the early 1900’s. Humanism was a response to the common educational theories at the time, which were behaviorism and psychoanalysis. Abraham Maslow is considered the father of the movement, with Carl Rogers and James F.T. Bugental adding to the psychology later down the line.

Maslow and the humanists believed that behaviorism and other psychology theories had a negative perception of learners—for example operant conditioning in behaviorism psychology suggested that students only acted in a good or bad manner because of the reward or punishment and could be trained based on that desire for a reward. Maslow and humanistic psychology suggests that students are inherently good and will make good decisions when all their needs are met. Humanistic psychology focuses on the idea that learners bring out the best in themselves, and that humans are driven by their feelings more than rewards and punishments. Maslow believed this and wrote many articles to try and demonstrate it.

This belief that humans are driven by feelings causes educators who understand humanistic psychology to focus on the underlying human, emotional issues when they see bad behavior, not to just punish the bad behavior. The humanistic learning theory developed further and harnesses the idea that if students are upset, sad, or distressed, they’re less likely to be able to focus on learning. This encourages teachers to create a classroom environment that helps students feel comfortable and safe so they can focus on their learning. Emotions are at the center of humanism psychology. 

The principles of humanistic learning theory.

There are several important principles involved in the humanistic learning theory that all lead to self-actualization. Self-actualization is when all your needs are met, you’ve become the best you’ve can, and you are fulfilled. While Maslow and the humanists don’t believe that most people reach self-actualization, their belief is that we are always in search of it, and the closer we are, the more we can learn. 

Student choice. Choice is central to the humanistic learning theory and humanistic psychology. Humanistic learning is student-centered, so students are encouraged to take control over their education. They make choices that can range from daily activities to future goals. Students are encouraged to focus on a specific subject area of interest for a reasonable amount of time that they choose. Teachers who utilize humanistic learning believe that it’s crucial for students to find motivation and engagement in their learning, and that is more likely to happen when students are choosing to learn about something that they really want to know. 

Fostering engagement to inspire students to become self-motivated to learn. The effectiveness of this psychology approach is based on learners feeling engaged and self-motivated so they want to learn. So humanistic learning relies on educators working to engage students, encouraging them to find things they are passionate about so they are excited about learning. 

The importance of self-evaluation. For most humanistic teachers, grades don’t really matter. Self-evaluation is the most meaningful way to evaluate how learning is going. Grading students encourages students to work for the grade, instead of doing things based on their own satisfaction and excitement of learning. Routine testing and rote memorization don’t lead to meaningful learning in this theory, and thus aren’t encouraged by humanistic teachers. Humanistic educators help students perform self-evaluations so they can see how students feel about their progress.

Feelings and knowledge are both important to the learning process and should not be separated according to humanistic psychology. Humanistic teachers believe that knowledge and feelings go hand-in-hand in the learning process. Cognitive and affective learning are both important to humanistic learning. Lessons and activities should focus on the whole student and their intellect and feelings, not one or the other.

A safe learning environment. Because humanistic learning focuses on the entire student, humanistic educators understand that they need to create a safe environment so students can have as many as their needs met as possible. They need to feel safe physically, mentally, and emotionally in order to be able to focus on learning. So humanistic educators are passionate about the idea of helping students meet as many of their needs as possible.

The role of teacher and student in humanistic learning theory.

In the humanistic learning theory, teachers and students have specific roles for success. The overall role of a teacher is to be a facilitator and role model, not necessarily to be the one doing the teacher. The role of the teacher includes:

Teach learning skills. Good teachers in humanistic learning theory focus on helping students develop learning skills. Students are responsible for learning choices, so helping them understand the best ways to learn is key to their success.

Provide motivation for classroom tasks. Humanistic learning focuses on engagement, so teachers need to provide motivation and exciting activities to help students feel engaged about learning. 

Provide choices to students in task/subject selection. Choice is central to humanistic learning, so teachers have a role in helping work with students to make choices about what to learn. They may offer options, help students evaluate what they’re excited about, and more. 

Create opportunities for group work with peers. As a facilitator in the classroom, teachers create group opportunities to help students explore, observe, and self evaluate. They can do this better as they interact with other students who are learning at the same time that they are.

Humanistic approach examples in education.

Some examples of humanistic education in action include:

Teachers can help students set learning goals at the beginning of the year, and then help design pathways for students to reach their goals. Students are in charge of their learning, and teachers can help steer them in the right direction.

Teachers can create exciting and engaging learning opportunities. For example, teachers trying to help students understand government can allow students to create their own government in the classroom. Students will be excited about learning, as well as be in-charge of how everything runs.

Teachers can create a safe learning environment for students by having snacks, encouraging students to use the bathroom and get water, and creating good relationships with students so they will trust speaking to their teacher if there is an issue. 

Teachers can utilize journaling to help students focus on self-evaluation and their feelings as part of learning. Using prompt questions can help students better understand their feelings and progress in learning. 

Best practices from humanistic theory to bring to your classroom.

A teaching degree is a crucial step for those who want to be teachers. A degree can help them learn about current practices and trends in teaching, learning theories, and how to apply them to the classroom. Established teachers can also greatly benefit from continuing education and continuously expanding their techniques. 

When considering their own teaching practices, teachers can work to incorporate humanistic theory into their classroom by:

Making time to collaborate with other educators

Co-planning lessons with other teachers

Evaluating student needs and wants regularly

Connecting with parents to help meet specific student needs

Preparing to try new things with students regularly

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What is the Humanistic Theory in Education?

Quick Definition of the Humanistic Theory in Education

Definition: The humanistic theory of teaching and learning is an educational theory that believes in teaching the ‘whole’ child. A humanist approach will have a strong focus on students’ emotional wellbeing and eternally view children as innately good ‘at the core’.

Assumptions of Humanism

A humanist educator’s teaching strategy will have four philosophical pillars. These pillars will guide the teacher’s beliefs and, ultimately, how they teach. These four pillars are:

• Free Will : We have free choice to do and think what we want;
• Emotions impact Learning: We need to be in a positive emotional state to achieve our best;
• Intrinsic Motivation: We generally have an internal desire to become our best selves;
• Innate Goodness: Humans are good at the core.

A Note on Referencing

As my regular readers know, all good essays should start with scholarly definitions . So, here are some scholarly definitions of humanism that you might want to use in your essay:

• Duchesne & McMaugh (2016, p. 263) argue that humanist theorists “consider the broad needs of children, including not just cognitive but also social and emotional needs.”
• Crain (2015, p. 363) points out that the focus of humanist psychology is helping people (humans!) to achieve their personal best. He argues that humanists “have proposed that people, to a much greater extent than has been realized, are free and creative beings, capable of growth and self-actualization.”
• Veugelers (2011, p. 1) argues that humanist education “focuses on developing rationality, autonomy, empowerment, creativity, affections and a concern for humanity.”
• Khatib, Sarem and Hamidi (2013, p. 45) argue that humanist education “emphasizes the importance of the inner world of the learner and places the individual’s thought, emotions and feelings at the forefront of all human development.”

Remember, it’s best to paraphrase definitions (e.g. put them in your own words ). Then, you should reference your sources at the end of your sentence or paragraph.

Here’s my attempt at paraphrasing the above scholarly definitions:

Humanism believes that a learner is free-willed, fundamentally good, and capable of achieving their best when the ideal learning environment is produced. The ideal learning environment should caters to the social, emotional and cognitive needs of the learner (Crain, 2009; Duchesne et al., 2013; Veugelers, 2011).

Now that you’ve got your definition and scholarly sources , don’t forget to include your references at the end of your essay:

Crain, W. (2015). Theories of Development: Concepts and Applications: Concepts and Applications. London: Routledge.

Duchesne, S. & McMaugh, A. (2016). Educational Psychology for Learning and Teaching . Melbourne: Cengage Learning.

Veugelers, W. (2011). Introduction: Linking autonomy and humanity. In: Veugelers, W. (Ed.). Education and Humanism: Linking Autonomy and Humanity (pp. 1 – 7). Rotterdam: Sense Publishers.

Khatib, M., Sarem, S. N., & Hamidi, H. (2013). Humanistic Education: Concerns, Implications and Applications.  Journal of Language Teaching & Research ,  4 (1), pp. 45 – 51.

The above references for the above sources. They’re in APA format, so if you are required to use another format like MLA, Chicago or Harvard, you’ll need to convert these to the correct style .

2. Origins of Humanist Education

In the early 20 th Century (early 1900s), behaviorism and psychoanalysis were the dominant educational theories. Humanists thought both these theories had very negative perceptions of learners. These theories tried to diagnose and ‘fix’ learners.

In reaction, humanistic education emerged. Humanists argued that people should stop seeing learners as ‘defunct’ or ‘in deficit’. Instead, humanists focussed on how we could help learners bring out the best in themselves.

Another thing humanism rejected was the assumption that learners were easily controlled by rewards and punishments. Humanists thought this ‘behaviorist’ approach of rewards and punishments failed to see that humans are complex thinkers. We’re driven by many different factors, and one major one is of course our emotions : how we’re feeling.

You can’t just punish someone when they do something wrong. No, no, no!

To humanists, you need to explore the factors underpinning their bad behavior. Maybe they’re cold, hungry or feeling unsafe! If we fix the underlying problem, the person will probably start behaving more appropriately.

So, humanists emerged largely as a reaction to the negativity and simplicity of behaviorist beliefs about childhood. If you want to learn more about behaviorism, check my post on behaviorism out here .

3. A Focus on Emotions

Our emotions are important to humanists. Emotions (or what we often refer to in educational psychology as ‘affect’) will shape how, what, when, and how well we will learn something.

If you’re grumpy, sad, frustrated or distressed, you’re probably not going to learn too well. When I’m worried about something, I spend all my time thinking about it – and I forget to concentrate!

So, humanists think we should pay attention to emotions and make sure our learners are feeling positive, relaxed and comfortable. These are emotions that will make us ready to learn.

In fact, humanists think other theorists like behaviorists, cognitivists and sociocultural theorists don’t pay enough attention to emotions. While other theories pay attention to things like social and cognitive (mental processes) learning, they seem to overlook that our emotions have a really important impact on how well we learn.

Humanists want to solve that problem. Below, you’ll read all about different ways in which they do this.

• Related Post: How emotions influence our learning.

4. Humans are Fundamentally Good

According to the humanistic theory of personality , we are all fundamentally good people, humanists argue. We’re not born evil or start out with evil intentions. And we all seem to want the best for ourselves and our tribe.

So why do people end up being bad, even evil?

Well, according to humanist theorists, people do bad things because they have not been nurtured the right ways.

When our fundamental needs as humans are not fulfilled, we might act out. When a young person is treated inhumanely in childhood, they may go on to act inhumanely as a response. When someone is hungry, they may get grumpy and act out. But, if we treat young people well and ensure their needs are cared for, they’ll be able to focus on being good, well-rounded and fulfilled human beings.

So, something really nice about humanism is that humanist teachers tend to see the good in their students. Even when a student is playing up, the teacher doesn’t hand out punishments to try to ‘fix’ the student. Instead, the teacher says “what needs aren’t being fulfilled here?”

• Related Post: How do you see children? Good? Evil? Innocent?

5. Abraham Maslow: Key Humanist Theorist

You need to know about Abraham Maslow. If you write an essay on humanist education and don’t mention him, expect to lose marks.

So here are some basics about Maslow to get you off on the right foot:

• Born in 1908 in New York City.
• Was an unhappy, unfulfilled child (did this impact his beliefs, do you think?)
• Was once a Behaviorist theorist who studied under the famous Edward Thorndike, but decided behaviorism didn’t say enough about the complex nature of human beings.
• Was a professor at Brooklyn College.
• Developed the famous Maslow’s Hierarchy of Needs.
• Died in 1970

I got the above information from this scholarly book:

6. Maslow’s Hierarchy of Needs

Maslow’s Hierarchy of Needs is a famous pyramid that shows the fundamental things that people need in order to be fulfilled in their lives.

Background to the Hierarchy: Maslow created the Hierarchy of Needs after examining the lives of a group of highly successful people including Mahatma Gandhi, Albert Einstein, Abraham Lincoln and Elanor Roosevelt. According to Maslow, each of these successful people had each of these needs fulfilled, which let them climb to the top of the pyramid and reach ‘self-actualisation’ (a sense of fulfilment). Maslow thought only about 1% of all people reached self-actualisation.

The hierarchy has two types of needs:

• Basic Needs: At the bottom of the pyramid are basic needs or what we sometimes call deficit needs or deprivation needs. When we don’t have these needs, we’re motivated to fulfil them by any means necessary.
• Growth Needs: Once we have our basic needs satisfied, we work on growth needs or what we sometimes call being needs or esteem needs. These are the needs that have to be met to reach self-actualization.

Maslow thinks all of us strive to meet the needs on his pyramid every day of our life, but only few of us make it all the way to the top. Supposedly, we can’t meet higher-up needs until we’ve successfully met the lower needs on the pyramid.

Here’s the hierarchy:

• Physiological Needs (a basic need): Not to be confused with ‘psychological’, physiological needs are the things that we physically need to survive. These include: water, clothing, shelter, and food.
• Safety, Protection and Security Needs (a basic need): We all need to be safe from harm in order to thrive in life. If we’re always looking over our shoulder to see if we’re going to get whacked, we’re less likely to concentrate on learning anything new!
• Belongingness and Love (a basic need): Once we have successfully met our physical and safety needs, we can start working on developing relationships. All humans need positive relationships to be fulfilled. This might include having a sense that you’re included and belong in a classroom, that you’ve got a loving family to go home to, and that you have a group of friends to lean on in times of trouble.
• Esteem (a growth need): ‘ Esteem’ means to be thought well of. You need to think well of yourself ( self-esteem ) but also have others think well of you. If you have low opinions of yourself, you’ll set low standards for yourself and never be able to climb higher up the pyramid.
• Self-actualization (a growth need): The need for self-actualization is the feeling that you want to become the best you can be, now that all your needs have been met. You can go on to pursue creative endeavours and succeed to the best of your abilities because you’re not busy fighting to have all your other needs fulfilled.

7. Examples of Maslow’s Hierarchy of Needs in the Classroom

Most teachers expect you to use examples in your essays . So, let’s take a look at some examples of each of Maslow’s needs in education:

a) Examples of Maslow’s Physiological Needs in the Classroom

• Food – If a child comes to class hungry, they may not be in a fit state to study. To address this, teachers can implement Crunch n Sip time, start a Breakfast Club , or develop a partnership with a local food bank .
• Water – Teachers can encourage students to drink water before entering the classroom or encourage students to have a clear water bottle sitting on their desk .
• Clothing – I once had a student who started coming to class wearing a hoodie in the middle of the summer. Why? Because her mother had recently left the family and the father wasn’t coping. He hadn’t done the washing in weeks and my poor student didn’t have any shirts to wear. The solution? Our school parents’ committee had a clothing collection that the student could ruffle through to find a shirt she liked, while I contacted the appropriate liaison officer to get the dad some support.

b) Examples of Maslow’s Safety Needs in the Classroom

• Safe from Guns – As an Australian-Canadian, I was shocked when an American Grade 1 teacher relayed how worried she was about gunmen entering her school. I knew it was a worry in an abstract sense for teachers in the US. But I’d never put myself in the shoes of a worried teacher who felt that it might happened to her students any day. I feel for the poor students who have to have this thought go through their minds while trying to study.
• Safe from Strangers – Most schools these days required all visitors to school grounds to head to the front office to get a nametag and sign-in. This is to ensure students feel safe and don’t have strangers walking in and out of their classrooms for no reason.
• Safe from Harm – Classrooms need to keep sharp or dangerous materials a safe distance from students. Cords that could trip students up and tables with splinters need to be replaced so students can concentrate on learning rather than being exposed to harm.

c) Examples of Maslow’s Belongingness Needs in the Classroom

• Memberships – Inclusion of students in table groups in class , afterschool clubs and class research groups can help give them a sense of ownership over the classroom.
• Democratic Class Rules – Students who create their own classroom rules may feel a greater sense of belonging and ownership in the classroom.
• Display Walls – Having exemplary artworks or photos of students on the walls of the classroom can make students feel as if the classroom is a place where they are included and belong.
• Diversity in class books – Students who are of minority backgrounds may feel as if their identities are underrepresented in the classroom. Diverse protagonists in books and diverse representation in imagery around the classroom can help students feel as if their identity is included and respected.

d) Examples of Maslow’s Esteem Needs in the Classroom

• Celebration of Successes – When a student succeeds, feel free to publicly promote that success. When students see their peers spoken about positively, your example may rub-off. Be the leader in having high regard (‘esteem’) for your own students.
• Promotion of Self-Belief – Encourage students to believe in their own abilities to succeed. Teach students about growth mindsets which emphasize that success comes from effort. When students internalize this attitude, they will begin to see themselves as powerful and capable learners.
• High Expectations – Set high expectations for students and praise them only when something is praiseworthy. If you overdo praise, students will not respond well – so give praise genuinely!

8. Strengths and Limitations of Maslow’s Hierarchy of Needs

Here are the pros of Maslow’s Hierarchy:

• A focus on emotions – There are not many educational theories that take into account students’ emotional (‘affective’) states. Maslow’s hierarchy helps to address this flaw.
• Clear and understandable – I can see several flaws in Maslow’s hierarchy (see next point) but it’s a good starting point for stimulating discussion about the importance of emotions in learning.
• A positive outlook – the hierarchy sees students as all having positive potential and able to climb to the top.

Here are the cons of Maslow’s Hierarchy:

• Linear – It is evident that people can succeed and learn in very troubling, difficult situations. Students can succeed through poverty, war and hardship to rise to become doctors and artists. Maslow’s hierarchy doesn’t take into account the fact that some people can learn despite some of their basic needs not being fully met.
• Methodologically Limited – Maslow developed the hierarchy by looking at a small subset of successful people. The hierarchy is not statistically relevant and lacks a clear evidence base.

(Note: later in this article there is also a list of general strengths and limitations of humanist theory overall, which you can jump to by clicking here.)

9. Carl Rogers: The other Humanist Theorist

Carl Rogers was another highly influential humanist theorist. If you’re writing an essay about humanism in education, I strongly recommend you also write about Rogers’ ideas.

Here’s an overview of Rogers’ key concepts:

• Actualizing Tendency: According to Rogers, we all have a tendency to strive toward personal growth. We all have ambitions to be better. Rogers called this an ‘actualizing tendency’, and used this concept to underpin his ideas about education.
• Freedom to learn: Rogers write the book Freedom to Learn which outlines how it is important for students to be freed from the constraints of a school curriculum in order that they can be free to explore things they are interested in. If we are freed to learn what we choose to learn (emphasis on free choice here!), we will learn things that our actualizing tendency (desire for self-improvement) lead us towards. This may mean we end up learning more, and learning things that are more important to us personally
• Unconditional positive regard : We have already seen from Maslow that humanists believe students need to have strong self-esteem (positive regard for themselves). Rogers believes that we can help students achieve stronger self-esteem by unconditionally seeing students in a positive light. Much like a parent who loves their child unconditionally, teachers have to see that their students are fundamentally good, even when they’re at their worst.
• Facilitation: Because humanists don’t believe there should be a set curriculum or learning outcomes, teachers become facilitators rather than authority figures . Teachers encourage students to seek new knowledge and provide the materials and support needed. This approach is very similar to the approach used in constructivist and sociocultural education.
• Intrinsic motivation: Rogers believes schools have historically repressed intrinsic motivation that we all had before we went to school. Here’s a great quote from Rogers:

“I become very irritated with the notion that students must be “motivated.” The young human being is intrinsically motivated to a high degree. Many elements of his environment constitute challenges for him. He is curious, eager to discover, eager to know, eager to solve problems. A sad part of most education is that by the time the child has spent a number of years in school this intrinsic motivation is pretty well dampened.” (Rogers, as cited in Schunk, 2012, p. 355).

Related Post: Intrinsic vs. Extrinsic Motivation in the Classroom .

10. Examples of Carl Rogers’ Humanistic Theory in the Classroom

If we are to follow Rogers’ humanistic teaching approach, we would do some of the following things:

• Not use a Curriculum: Throw out the learning outcomes and help students learn things that are motivating and inspiring in their own lives.
• Encourage Choice: Ask the students not only how they want to learn but what they want to learn.
• Encourage Inquiry Learning: When students have chosen a topic to learn about, give them rich resources and an inquiry-based learning environment so students can explore their interests without having them stifled by nasty worksheet printouts!
• Act as a Facilitator: Don’t stand out the front of the class and teach in a teacher-centered manner that you might find from behaviourist theory. Instead, facilitate learning by creating the right learning environment for students to explore.
• Express Unconditional Positive Regard: Even when students are playing up, we need to have positive regard for our students by being empathetic, positive and supportive as educators . Our language should show students we have high regard for them: “This is not like you, I know you as a lovely person usually!”, “Let’s start tomorrow fresh and believe in ourselves that tomorrow will be a better day where you go back to being your well-behaved self.”

11. Strengths and Weaknesses of the Humanist Theory of Education

Strengths of humanism in education include:

• Unlike many theories that attempt to diagnose weaknesses, humanism sees the best in everyone and works hard to promote it;
• It is an empowering philosophy that sees young people as powerful and capable;
• It considers emotional states and how they impact learning, unlike many other theories;
• It is holistic, meaning it sees the ‘whole child’. It will look at cognitive, social and emotional aspects meaning it has many pedagogical overlaps with cognitive and social constructivist theories, but also adds the ‘emotional’ elements;

Weaknesses of humanism in education include:

• It does not follow a set curriculum. This aspect of humanism may be incompatible with contemporary schools which usually have a standardized curriculum that students need to learn from;
• If it were implemented in schools, every student would leave school having different knowledge. Sometimes students need to learn things like mathematics even if they don’t have intrinsic desire to learn about it!
• Some students require structure and routine to learn effectively. With its emphasis on choice-based learning, aspects of humanism may not work well for such students.

Related Motivation Theories:

• Expectancy-Value Theory
• Self-Determination Theory
• Keller’s ARCS Model of Motivation
• The ABC Model of Attitude

Cite these Sources in your Essay

Don’t forget that you need to cite scholarly sources in your essays!

Here’s the APA style citations for some sources I used when writing this article:

Bates, B. (2019). Learning Theories Simplified: …and how to apply them to teaching. London: Sage.

Schunk, D. H. (2012). Learning Theories: An Educational Perspective. Boston: Pearson Education.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 50 Durable Goods Examples
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Humanistic Learning Theory – Teaching Students to Reach Their Full Potential

The Humanistic Learning Theory is a whole-person approach to learning where the focus is to help students become their best selves.

• By Paul Holt
• Oct 2, 2023

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• The humanistic approach to learning is based on the idea that human beings are inherently good and intrinsically motivated.
• According to the Humanistic Learning Theory, students should be responsible for setting their goals and evaluating their progress.

Our main goal as educators is to help our students thrive to increase the possibility of each student reaching their full potential. We want our kids to become functional adults, confident in their abilities, and contributing to the community. The humanistic approach to learning shares the same ideals and goals. Unlike some of the learning theories popular today, this approach focuses on educating the student’s “whole being,” not just the intellect.

So, what does it mean to educate the “whole being”? In this article, we’ll discuss Humanism and how it applies to education. In addition, we’ll look at the benefits and drawbacks of the Humanistic Learning Theory and how this approach can be applied in the online classroom.

Table of Contents

What is humanism.

Humanism is a philosophy that can be traced back to Ancient Greece. It is based on the idea that people are inherently good. The only reason why people do bad things is because their needs aren’t met. Humanism also places importance on human dignity and values – everyone has worth. Moreover, it believes that humans have the ability to control their environment, shaping it according to their needs. It further believes that a person’s potential for growth and development is unlimited.

What is the Humanistic Learning Theory?

So, what does humanism have to do with learning? Well, humanism’s view on education can be broken down into several foundations:

• Free will: Everyone has the freedom to do what they want.
• Innate goodness: Everyone is innately good “at the core,” and we will always want the best for ourselves and for others.
• Positive emotions: Feelings cannot be separated from intellect. A person needs to feel positive and relaxed in order to be open to learning and achieve the best results.
• Intrinsic motivation: Humans are born with the motivation to become the best version of themselves.

Based on these philosophical pillars, we can define what Humanistic Learning Theory is all about. First, it is an approach to education that is centered on the learner . The learner has freedom and autonomy. This means that much of what a person learns and how he learns is based on his choice, not on the teacher’s preferences.

Second, student-centered education includes student-led evaluation , which means that students are responsible for evaluating their progress. When evaluation is based on a grading system, students are encouraged to work hard to earn a high grade instead of being passionate about what they’re learning. According to this theory, rote memorization and routine testing do not promote learning. Self-evaluation , on the other hand, enables students to feel satisfaction and excitement about what they’ve accomplished. Students will focus on improving themselves based on the standards that they have set.

In addition, we all have an intrinsic desire to become our best selves. According to the Humanistic Learning theory, this means that students need to be engaged in the learning process . The desire to learn should come from the students. This means that teachers need to cultivate their curiosity and encourage them to pursue their interests. Once motivated, students will become active participants in the learning process and develop a love for learning.

Moreover, since we are all fundamentally good, teachers who use this approach should not view students who misbehave as “bad.” They also do not dole out punishments or negative feedback in order to fix or correct a behavior. Instead, they need to determine which needs aren’t being fulfilled, causing this type of behavior and preventing the student from reaching his full potential.

Educating the person as a whole

Learning, as defined by this theory, is the growth of a person as a whole. This means that the learning process needs to consider more than just the knowledge a student needs to acquire. The learner’s needs and desires, as well as his emotional state, are equally as important because these can impact learning.

In addition, to unlock a learner’s full potential, teachers need to consider and educate the “whole” person. Teachers should focus on both cognitive and affective learning, putting equal importance on feelings, artistic skills, social skills, self-esteem, and practical skills. Learning is not just about intellect.

Let’s make it easier to understand: When a student is feeling anxious or upset, it’s highly likely that he won’t be able to pay attention in class. Not only could this become a hindrance to the learning process, but it can also cause them to behave badly. Teachers will need to teach students how to deal with anxiety and how to overcome negative emotions in order for them to perform well. In addition, they need to create a learning environment where students feel safe and comfortable.

Here’s an example: Hunger, a basic human need, can affect a student’s attention, making it difficult for him to understand and/or engage in learning activities. This means that schools need to make sure that students are getting their basic needs met while they’re within the halls of the institution (for example, by providing meals in the cafeteria).

Advantages of the humanistic approach to learning

Applying the Humanistic Learning Theory in the classroom has several advantages:

• It promotes positivity in the classroom.
• This approach empowers students to motivate themselves to become the best version of themselves.
• Unlike many other learning theories, the Humanistic Learning Theory is a holistic approach that considers the emotional state of the students and how this can affect their learning.
• The humanistic approach to learning is inclusive of everyone. Learning is focused on the individual, not the group, giving every child the opportunity to succeed.

Disadvantages of the Humanistic Approach to Learning

Like other learning theories, there are also disadvantages to using this approach in the classroom:

• Because the source of authority is the student, the curriculum needs to be less fixed, which may not be compatible with traditional schools.
• This approach requires students to pursue their interests. However, not everyone will have an intrinsic desire to study subjects like mathematics and physics, potentially setting them up for future challenges.
• Routine and structure are necessary for some students to learn effectively. Freedom of choice and authority in learning may not be suitable for these students.

Applying the Humanistic Learning Theory in the online classroom

Because learning is student-centered, the role of the teacher is to be a facilitator and model. The teacher is responsible for helping students develop learning skills, feel motivated and engaged, and provide them with different topics, materials, and tasks to choose from. As a facilitator, teachers also need to create opportunities for students to work with their peers to practice social as well as practical skills.

Some examples of implementing the key principles of humanism in the online classroom include:

• Teachers can help students create goals at the start of the school year. Make sure that you take time to discuss what they want to achieve and how they can achieve them.
• Provide different learning opportunities that cater to the interests and learning styles of the students. For example, students can choose to complete practical activities, watch videos, conduct research on the web, and/or participate in online discussions (e.g., social media interaction, online forums).
• Use gamification to increase engagement and motivation. Instead of prizes, students go up levels and earn experience points, which can help promote a student’s intrinsic desire to improve. In addition, levels and experience points make it easier for students to evaluate their progress. Moreover, this can help them develop a love for learning.
• Give students flexibility in their schedules and modes of participation. Some students learn better in the morning, while others are more efficient later in the day. Some may want to join an online class, while others prefer to do their online lessons on their own. Teachers can look for adaptive software that allows independent self-paced study. Doing this also encourages independent responsibility.
• Create a safe, comfortable learning environment by allowing students to take snack and bathroom breaks when they need them. In addition, encourage them to evaluate and share their feelings because these can impact learning. Cultivate a classroom culture where everyone is accepted and emotional experiences can be shared through honest discussions. If needed, you can create a safe or brave space where they can tell you how they feel privately, such as a breakout room in Zoom .
• Integrate problem-based learning and inquiry-based learning techniques to help students practice critical thinking and feel a sense of accomplishment. Don’t forget to celebrate their milestones to give them recognition and increase their motivation.
• Practice empathy and respect. Prepare for moments when students are experiencing high levels of stress or anxiety. Be ready to listen and become a support system. This may be harder when classes are conducted online; the distance can feel like a barrier. Developing a good relationship with the students and keeping in touch with them online outside of school hours can help them trust you with their feelings.

The Humanistic Learning Theory tells us that learning goes beyond the intellect. The focus is on the student and helping him reach his full potential. The greatest contribution of this theory in the classroom is that it promotes independence, a love for learning, and a motivation for self-growth. As teachers, it is our privilege to help them become the best version of themselves, confident people who make meaningful contributions to our society.

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Getting smart collective, impact update, 4 holistic classroom ideas inspired by maslow’s humanist approach.

By: Chris Drew

Too often educators look at students as being ‘in deficit.’ Deficit thinking can be evident in schools that focus too heavily on reforming student behavior rather than creating positive learning climates that bring out the best in students.

This mentality was also dominant amongst educational psychologists in the mid-20th century. However, during this period, humanist theorist Abraham Maslow began to promote a novel idea. Maslow’s humanist perspective  emphasized the importance of promoting the innate goodness inside all people.

From Maslow’s perspective, goodness needs to be nurtured by providing learners with a safe and fulfilling learning environment.

Perhaps Maslow’s most influential idea was his hierarchy of needs. To Maslow, we all have a range of needs that should be met in order to bring out the best in ourselves. Those needs are:

• Physiological needs : At the base of Maslow’s pyramid are the physical requirements for life, including food, water and clothing.
• Safety needs : Students need to feel safe and secure in order to focus on learning.
• Belongingness needs : A feeling of inclusion and membership in a classroom community can help students enjoy coming to school.
• Esteem needs : Students need to feel their teachers and peers have positive regard for them. Similarly, students should feel good about themselves and their own ability to succeed.
• Self-Actualization : Also known as self-fulfillment, this need is met when students are achieving to the best of their abilities in the classroom.

Maslow’s hierarchy remains incredibly relevant to educators today. Below, I outline four ways Maslow’s hierarchy can inspire holistic education.

1. Start a Breakfast Club

Researchers from the University of Leeds in England reported in 2012  that 14% of students skip breakfast on a regular basis. Similarly, studies from the United States report 8-12% of children  turn up to school hungry.

Furthermore, students who skip breakfast are disproportionately  from disadvantaged backgrounds and single-parent households.

Those students who skip breakfast can suffer socially, emotionally and academically throughout the school day. By contrast, researchers from Northumbria University  highlight that students who participated in a breakfast club initiative in England self-reported increased capacity to focus in class and moderate their moods.

Thus, school-run breakfast clubs have the potential to support children’s learning by satisfying physiological needs which sit at the foundation of Maslow’s hierarchy.

By providing students with breakfast at the start of the school day, schools can proactively prevent potential issues that arise from low energy levels, fatigue and the inability to complete set tasks.

2. Create Brave Spaces

Maslow emphasizes that people need to feel safe in order to achieve self-actualization. Feeling safe in the classroom is more than simply about a sense of comfort. Rather, it is about feeling safe to step outside of your comfort zone with the knowledge that risks are accepted and encouraged.

Young people who feel insecure or fear harsh punishment from their teachers will be less inclined to be bold or take risks. Therefore, teachers should explicitly promote brave spaces: spaces in which young people know that risk taking, creativity and boldness are rewarded.

Colorado teacher   Leticia Guzman Ingram  writes that teachers should help students feel safe to make mistakes. She recommends strategies such as ‘Failure Fridays’ where the teacher and students discuss failures people have made and how the failures made them better learners.

3. Give Classroom Ownership to Students

To satisfy what Maslow calls ‘Belongingness needs’, teachers need to help students feel as if they are co-owners of the classroom space.

One way to promote a sense of ownership is to reinforce to the students that the classroom is not ‘mine’ but ‘ours.’ Every student should see a part of themselves in the classroom and be able to tell outsiders about how they were integral to creating a positive classroom culture .

My measure of success for creating belongingness is when I see students leading their parents by hand around the classroom, pointing out displays and telling stories of their creation. I want to see students pointing to one element of each display and showing how their small contribution helped in the creation of the whole.

4. Use Inquiry-Based Learning

Carl Rogers, a humanist contemporary of Maslow, also believed that self-fulfillment would only occur when a student is freed to explore topics that intrinsically motivate them. For this to be achieved, educators should allow students the freedom to choose topics of personal interest and identify ways to explore those topics in depth.

Open-ended, interest-based learning is increasingly difficult in an era of standardized curriculum requirements .

However, I like to give my students as much freedom as possible within the confines of the curriculum by, for example, finding ways curriculum topics overlap with their hobbies.

Educating the Whole Child with a Humanist Approach

I find many educators gravitate toward humanism. It is a theory that affirms that all students are capable of becoming their best so long as educators pay attention to the whole child. We need to attend to our students’ needs, feelings and emotions to ensure their basic needs are met.

When we turn our attention to our students’ needs, we not only embrace a caring approach to education but also create the conditions for supporting each student’s cognitive and social development.

For more, see:

• Educating the Whole Child Through PBL
• Why Every 10-year-old Should Know Maslow’s Hierarchy
• Students’ Basic Needs Must Be Met Before They Can Learn Deeply

Stay in-the-know with innovations in learning by signing up for the weekly Smart Update .

Chris Drew teaches about educational theory and practice at Swinburne Online University. He writes on educational topics on his personal blog, The Helpful Professor .

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Humanism Learning Theory and Implementation in the Classroom

Back to: Learning and Teaching – Unit 2

• The five basic principles of humanistic education can be summarized as follows:
• Students’ learning should be self-directed.
• Schools should produce students who want and know how to learn.
• The only form of meaningful evaluation is self-evaluation.
• Feelings, as well as knowledge, are important in the learning process.
• Students learn best in a nonthreatening environment.

Definition of Humanism Theory

Abraham Maslow, along with Carl Rogers, Malcolm Knowles, and many others propounded the humanism learning theory which focuses on the development of learners. He is considered as the father of Humanistic Psychology. The theory believes in encouraging learners to develop an interest for learning. Maslow believes that experience play a key role in influencing the learning and behaviour of humans. This theory is highly centered on learners. In this theory, teachers serve as role models. They motivate the learners who are required to be observant and keen to explore.

Scholar Definition:  The Humanist Teaching and Learning Theory is an educational theory that believes in the teaching of the child “as a whole”. The humanistic approach will pay special attention to students’ emotional well and will always consider children “essentially” good children.

Humanist theory:

Humanism stresses the importance of human values and dignity. It proposes that people can resolve problems through science and reason. Rather than looking to religious traditions, humanism focuses on helping people live well, achieve personal growth, and make the world a better place.Examples of humanistic behavior are everywhere. Everything from being kind to a stranger to scuba diving could be humanistic behavior if the motivation is a desire to live a good, authentic, and meaningful life.

Today, people call  Petrarch  the “ father of humanism ” and even the “first modern scholar.” Petrarch’s humanism appears in his many poems, letters, essays, and biographies that looked back to ancient pagan Roman times

Basic Principles of Humanistic Education

Students should be able to choose what they want to learn. Humanistic teachers believe that students will be motivated to learn a subject if it’s something they need and want to know.

The goal of education should be to foster students’ desire to learn and teach them how to learn. Students should be self-motivated in their studies and desire to learn on their own.

Humanistic educators believe that grades are irrelevant and that only self evaluation is meaningful. Grading encourages students to work for a grade and not for personal satisfaction. In addition, humanistic educators are opposed to objective tests because they test a student’s ability to memorize and do not provide sufficient educational feedback to the teacher and student.

Humanistic educators believe that both feelings and knowledge are important to the leaming process. Unlike traditional educators, humanistic teachers do not separate the cognitive and affective domains.

Humanistic educators insist that schools need to provide students with a nonthreatening environment so that they will feel secure to learn. Once students feel secure, learning becomes easier and more meaningful.

Implementation in the Classroom and Educational Implications

Humanism theory of learning plays an important role in enhancing the knowledge of the learners by fulfilling the ultimate goal of teaching.

Implications of instruction:

• Instruction should be intrinsic rather than extrinsic; instructional design should be student centered.
• Students should learn about their cultural heritage as part of self-discovery and self-esteem.
• Curriculum should promote experimentation and discovery; open-ended activities. .
• Curriculum should be designed to solicit students’ personal knowledge and experience. This shows they are valuable contributors to a nonthreatening and participatory educational environment.
• Learned knowledge should be applicable and appropriate to the student’s immediate needs, goals, and values.
• Students should be part of the evaluation process in determining learning’s worth to their self-actualization.
• Instructional design should facilitate learning by discovery.
• Objectives should be designed so students have to assign value to learned ideals, mores, and concepts.
• Take into account individual learning styles, needs and interests by designing many optional learning/discovery experiences.
• Students should have the freedom to select appropriate learning from many available options in the curriculum.
• Allow students input in instructional objectives.
• Instruction should facilitate personal growth.
• The implementation of humanistic theory in the classroom should take place in the following manner:

Curriculum Must Be Learner-centered

The curriculum should be based on the interest of the learners and should focus on their overall development. Their personal experiences and knowledge must be taken into account.

Knowledge Should Be Applicable

The knowledge being imparted to learners must be applicable in real life situations. They should be able to relate the lessons being taught with real life situations.

Emphasize Learner Development

Teachers must focus on all the round development of learners. Instructional methods should be such that it is comprehensible to learners and fosters their growth and development.

Learning Must Be Student-centered

The teaching learning method should be student-centered and they should also be included in the evaluation process for self actualization.

Experiments and Discovery Must Be Used

Since the humanistic approach to learning focuses on learning by discovery, experiments should be implemented in the curriculum to facilitate the same.

Learning Must Happen Through Discovery

The instructional method being used by the teacher in the classroom must facilitate learning through discovery.

Teaching Should be Intrinsic

Instead of adopting extrinsic teaching methods, teachers should adopt intrinsic teaching methods so that the teaching learning method can be student centered.

STUDENT’S ROLE

• The student must take responsibility in initiating learning; the student must value learning.
• Learners actively choose experiences for learning.
• Through critical self-reflection, discover the gap between one’s real and ideal self.
• Be truthful about one’s own values, attitudes and emotions, and accept their value and worth.
• Improve one’s interpersonal communication skill.
• Become empathetic for the values, concerns and needs of others.
• Value the opinions of other members of the group, even when they are oppositional.
• Discover how to fit one’s values and beliefs into a societal role.
• Be open to differing viewpoints.

TEACHERS ROLE

• Be a facilitator and a participating member of the group.
• Accept and value students as viable members of society.
• Accept their values and beliefs.
• Make learning student centered.
• Guide the student in discovering the gap between the real and the ideal self, facilitate the student in bridging this gap.
• Maximize individualized instruction.
• To facilitate independent learning, give students the opportunity to learn on their own ~ promote open-ended leaming and discovery.
• Promote creativity, insight and initiative.

The humanistic approach mainly focuses on the development of learners due to which factors must be remembered while implementing this approach in the classroom.

Why Schools Need to Change Purpose and Problem-Solving: Developing Leaders in the Classroom

Taiwo A. Togun (he, him, his) Faculty, Pierrepont School, and Co-Founder & Executive Director, InclusionBridge, Inc. in Connecticut

Today’s learners face an uncertain present and a rapidly changing future that demand far different skills and knowledge than were needed in the 20th century. We also know so much more about enabling deep, powerful learning than we ever did before. Our collective future depends on how well young people prepare for the challenges and opportunities of 21st-century life.

As educators transform learning in their classrooms, they can develop their students ’ talent and their own leadership while also making a difference for their community.

“Purpose is a stable and generalized intention to accomplish something that is at once meaningful to the self and consequential to the world beyond the self” –Bill Damon, Professor of Education, Stanford University

As an educator, my purpose is to nurture and develop young talents. While I have been teaching for over a decade, I only articulated my purpose as an educator last year during my master’s program in technology leadership while learning to integrate technology, strategy, and leadership. Coincidentally, I became a Project Invent fellow at the same time, which only served to embolden my sense of purpose. Clarity of purpose is a vital leadership quality that shapes my experience and something I believe ought to begin every teacher’s leadership journey. While one’s articulation of purpose may change over time, there’s something quite powerful and differently effective about writing down and reading out loud your purpose statement. In the following reflection, my goal is to share how I approach my development as an educator and a leader as one and the same and how my experience with Project Invent’s design thinking curriculum represents a continuing education in leadership.

Developing a Leadership Identity

As I work toward establishing my leadership identity and persona as an educator, I find myself reflecting on Sun Tzu’s Art Of War in which he described “ Leadership [as] a matter of intelligence, trustworthiness, humaneness, courage, and discipline. ” Additional discourses from the likes of Thomas Carlyle , Tolstoy , and Plato have all helped me arrive at an understanding of leadership as a function of nature, nurture, and situation . In addition to clarity of purpose, other leadership qualities must be deliberately nurtured through training and cultivated through practicing acts of leadership. I believe an effective leader empowers others and recognizes situations when the act of leadership is, in fact, letting others lead. This summarizes the core takeaway of my “teacher as a leader” philosophy.

In 2021, I applied to Project Invent’s educator fellowship , hoping to reinforce my leadership identity as an educator. Project Invent is a nonprofit organization that trains educators in six key teacher practices, each aimed at empowering students with the mindsets to become fearless, compassionate, and creative problem solvers. As a Project Invent Fellow, I have made significant progress in mastering these six teacher practices:

• Make failure okay
• Push to the next level
• Be a co-learner
• Let students take the wheel
• Leave room for exploration
• Challenge assumptions

Courtesy of Project Invent

Leadership in Practice

Each of these teacher practices can occur independently but are often interrelated. Deliberately committing to one can undoubtedly lead to others. For example, being comfortable with being a co-learner allows space for leaving room for exploration of alternatives. Openness to the possibility of new alternatives begets making failure okay and also encourages letting students take the wheel and drive the process, while the teacher-leader nudges them to push to the next level. Of course, the order of these is not fixed.

I teach computer science at Pierrepont School in Westport, Connecticut. My Project Invent student teams come from two classes of juniors and seniors, who originally signed up for an Applied Data Science course. We began our journey in the second semester in January, after which the students were informed that their course name had changed from “Applied Data Science” to “Essential Skills of the Emerging Economy” which has two parts: “Critical Reasoning & Storytelling with Data” and “Human-centered Problem-solving.” These are the only details my brave students had to work with. Needless to say, students had to be open-minded about how the journey would shape up. After all, it is not the first time that I would modify course requirements to marry interests and new opportunities that would benefit my students. I enjoy such flexibility and reasonable autonomy at my school; I also enjoy the flexibility and reasonable autonomy of learning as I teach. I am comfortable admitting to my students that I have absolutely no idea how to solve a challenge that I assign them, but assure them we can figure it out together… and we always do.

In January, the challenge was dauntingly ambiguous: We were going to invent a new technology intended to positively impact members of our community. Given their awareness of how little I knew about what we might need, or how to invent anything for that matter, students had to buy into taking a journey with an uncertain destination together. My job as a co-learner was to make sure to emphasize that it was all about the journey, the lessons, and the fun we have; and not necessarily the end. The humility and willingness to be a co-learner with students in the driver's seat have served me very well throughout my journey as a teacher, and I can not begin to describe the gratification of learning with and from students and seeing them rise to the challenge. This time, however, we had access to a community of resources, fellows, and mentors through the extended Project Invent team, who made it even more reassuring despite the many unknowns. From the onset of our journey, my students demonstrated creative confidence and trust in one another (most of the time) and our system as a class. Together as a team, we were ready and excited for the journey.

“Coming into this class with a limited computer science background, I was a little intimidated to embark on a project that had the potential to create such a big and meaningful improvement in our community. However, as I grew more comfortable with my team, my fears eased. I was able to develop from a quiet listener to a confident doer, not only for the duration of this project but also in my longer-term data science pursuits.” –Alexis Bienstock, Pierrepont School Junior

Project Invent as Context for Leadership Development

Human-centeredness brings a new dimension to problem-solving. It helps to establish and define a worthy purpose. My students and I began our journey on our Project Invent experience by getting to know our “client” Roderick Sewell , a Paralympic athlete and swimmer, as a person—what he enjoys doing, how he got to become a serious athlete, and what his goals and aspirations are. We focused on his abilities, accomplishments, and strengths. This set the stage for helping us—students and teachers alike—cultivate mindsets of empathy and curiosity. It is this empathic curiosity that would eventually lead to two Project Invent teams of ambitious students, who set out to address Roderick’s expressed challenges of lower back pain and efficient switch from running to walking legs:

“Because there’s nothing to absorb the load except for my lower back…If there was a little more cushioning on the soles to absorb the impact, then everything would be even more doable.” “ I can’t really run with my walking leg. One question that I always have is if something happened, how fast would I be able to get up and get away? ” –Roderick Sewell

Team SNAILS, a team of one senior and five juniors, proposed and prototyped an invention they called Quick Switch Support Shoe (“QS-cubed”), a multifunctional prosthetic foot support with adjustable springs to minimize back pain and maximize run-walk efficiency for their community partner.

Team Pierrepont Innovators with three seniors and four juniors had the ambitious goal of completely redesigning Roderick’s prosthetic ankle with a dashpot or snubber mechanism and incorporating more effective shock-absorbing materials. They wrestled with disappointments as they came to terms with reality and time constraints, and the team eventually demonstrated resilience and agency as they made a pivot to capitalize on their research of Shock-absorbing materials. They developed a pitch to prosthetic companies which can incorporate their research insights to further possible impact.

The larger purpose of our 10-week journey into design thinking was our connection with Roderick’s expressed discomfort. This purpose shaped our introduction to need-finding, synthesizing and ideation, idea selection and prototyping, prototype refinement, and pitching. Students persevered through their fears, disagreements, and disappointments; they made it work because they did not think it was about them but rather about what they could contribute to support Roderick.

“Our community partner Roderick Sewell is the first bilateral above-the-knee amputee to finish the IRONMAN World Championship. As a serious athlete, he needs to feel his best to perform his best—and that’s our charge!” –Team Pierrepont Innovators

“Working on Project Invent provided me with an appreciation for Roderick Sewell and the time I spend with my classmates. The opportunity to learn Roderick’s story as we worked with him to develop solutions to his lower back pain proved to be the most rewarding part of the process.” –Hagen Feeney, Pierrepont School Senior

Understanding the Journey

“He who has a why to live for can bear almost any how.” –Friedrich Nietzsche

By default, as educators we teach process; learning to solve problems in several different ways is central to our training, and sometimes that dominates our lessons to students. The Project Invent experience helps educators and students alike to prioritize the “why” and “what” of our learning over the “how.” The Project Invent experience added the very essential element of “purpose” which helped my students and me push the boundaries of the typical project-based, creative problem-solving classroom experience. Indeed, such an experience only thrives in and helps to foster a culture of caring, purpose, learning, and enjoyment (all in the dimension of flexibility to respond to change)—the kind of culture espoused by our school, Pierrepont culture ! Through our experience with human-centered problem-solving, students and teachers alike have cultivated practices and mindsets that are necessary to become leaders.

Every Leader Needs a Community and a Support System

“Leadership without support is like trying to make bricks without enough straw. True leaders reinforce their ideas and plan with strategic partnerships, alliances, and supportive audiences.” –Reed Markham, Ph.D.

In addition to the Pierrepont culture that presented a fertile soil for the teacher practices and students’ mindsets we needed, the Project Invent community and support system were so important for us. I recall the confidence boost and reassurance from our first session with a volunteer expert, Valerie Peng, an engineer who builds robots for a living. Not only did my team get to soak invaluable information that was relevant for advancing our project, but we were also all inspired by the passion with which she shared her work with us. Similarly, I found renewed strength and motivation with each conversation with Project Invent staff members and other fellows. In our shared space as educator-leaders, my co-fellows and I were able to explore possible solutions to shared challenges like keeping students motivated through their fears and disappointments, navigating operational logistics and schedule challenges, etc. I am indeed grateful for such a community as it helps to know you are not alone.

Beyond the Classroom

The teacher as leader practices cultivated during my Project Invent experience has affected my work beyond Pierrepont. With clarity of purpose and the necessary focus on impact and human-centeredness, my data science consulting company has embarked on a renewed mission to diversify the data science workforce and bridge the gap to full and equal participation in the emerging digital economy through InclusionBridge . Indeed, the Project Invent experience provided a complementary lens for me to refine my purpose—my journey—of nurturing and developing young talents through problem-solving and meaningful learning experiences. I enjoy creating and facilitating opportunities to help students become fearless, compassionate young leaders.

Image at top is a slide from the student project presentation by Team SNAILS, Pierrepont School.

Taiwo A. Togun (he, him, his)

Faculty, pierrepont school, and co-founder & executive director, inclusionbridge, inc..

Taiwo is an educator, a data scientist, and a social entrepreneur who is passionate about nurturing and developing young talent. He is the architect and director of the Computer Science program and Innovation Lab at Pierrepont School , a private K-12 where he enjoys the challenge of making computer programming and problem-solving skills accessible to students at all levels. Dr. Togun is a visiting scientist at the Boykin Lab at the Department of Cognitive, Linguistic, and Psychological Sciences at Brown University, supporting research to elucidate perceptions of fairness in machine learning algorithms. With a Ph.D. in computational biology & bioinformatics from Yale and a master's in technology leadership from Brown, he combines data science, technology, strategy, and leadership as co-founder and executive director of InclusionBridge . Through InclusionBridge, Taiwo and his team are on a mission to increase diversity in the data science workforce through internships and training programs for underrepresented talent. Follow Taiwo on LinkedIn .

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Humanistic Strategies in the Classroom

Victoria thompson.

The humanistic classroom provides a holistic approach to learning by keeping the focus on the child. The student is respected as an individual and is responsible for making decisions about his learning. Humanistic lessons are not rigidly prescribed, but flow according to the needs and inquiries of the student. This open approach helps provide emotional support for the student in a humanistic classroom.

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• Student-Centered Learning
• Emotional Support
• Open Seminars
• Cooperative Learning
• Discovery Education

1 Student-Centered Learning

Student-centered learning takes place when the teacher becomes a facilitator, taking the focus from herself as the bearer of knowledge. The student takes on an important role in this type of classroom. Lessons originate and develop from the interests of the student. The child is able to showcase his creativity in this type of open classroom, which increases self-esteem and a willingness to learn.

2 Emotional Support

A humanistic classroom is inclusive of everyone. This type of class seeks to support both individuality and diversity by finding the similarities among children. Lessons are developed not for the group, but for the individual. Diversified lessons give each child a chance to succeed and receive positive reinforcement. Each child knows how it feels to succeed, and stratification of students is eliminated. Each child learns at an individual pace without labels and stereotypes that can stigmatize.

3 Open Seminars

Open seminars provide a chance for the student's voice to be heard. Situating desks in a circle, with the teacher joining the circle, gives everyone an equal voice. There should be rules for the open seminar, such as respect of opinions and giving each person a chance to speak without interruption. The seminar may focus on a question from a student, a piece of literature, a current event or anything the class is studying.

4 Cooperative Learning

Cooperative learning lets children work together to find solutions to problems. Each child may have a specific role within the group to make use of his talents. The teacher supervises each group of about three or four students to answer questions and provide support. This type of learning allows the student to learn how to foster peer relationships, an important skill to carry throughout life.

5 Discovery Education

In discovery education, the teacher introduces a concept and gives the student freedom to discover her own path to learning more about the concept. This strategy supports the concept of multiple intelligences and intellectual diversity. Abstract learners may seek books and computers to research the concept. The interpersonal personality may seek out others to question for information on the topic.

• 1 Gary Sturt: Humanistic Approaches to Teaching

About the Author

Based in North Carolina, Victoria Thompson has taught middle school for the past 15 years. She holds a Masters of Education in middle school instruction from the University of North Carolina at Greensboro. She teaches English daily to English as a second language students.

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What is Humanism

What is humanism.

Humanism is a highly effective approach to human development. It is as concerned with personal development as it is with social responsibility. It is probably the most humane and holistic approach to ethical philosophy humanity has ever devised.

One way to think of Humanism is that it is the practice of putting your reason and compassion into action.

The Values of Humanism

Humanist values are common human values. These are values humans all over the world share. People of every faith background and no faith background, generally agree that these values are important. This is what makes Humanism such a powerful approach to thinking about values in the workplace. It’s a values based approach that is accessible to everyone.

This value wheel from the American Humanist Association lists 10 Humanist commitments. These are commitments we make to ourselves as we try to live responsibly by our values.

• Critical Thinking
• Ethical Development
• Peace and Social Justice
• Service and Participation
• Environmentalism
• Global Awareness
• Responsibility

You can learn more about these commitments and what they mean – here: https://americanhumanistcenterforeducation.org/?page_id=14747

The Humanist Approach to Learning

The  Humanist approach to learning is to be flexible, rational, compassionate and responsible. Our goal is to be the best most ethical people we can be but humble enough to understand that regardless of how good we may be, we can always improve.

Humanist Learning Systems programs focus on teaching the three elements of the Humanist philosophy that are most applicable to problem solving and improving our interpersonal relationships which are at the root of most of our problems. The three subjects are:

• Compassion Based Ethics
• Personal Responsibility.

Our goal is to provide quality programs that will help you learn how to apply your values to every day problem solving and why approaching problem solving with your ethical values front and center yields superior results. We know from personal experience that the more we practice living our values and approaching problems with love and dignity, the more effective we are and the more accomplished we feel.

There is a reason every religion and philosopher throughout history teaches love and compassion. It’s an optimistic way to approach life and it reduces anxiety. The more you learn how to practice it. The simpler life becomes.  The best part, all that is required is  … practice.

The Power of Applied Humanism

The various skills and techniques of Humanism when applied to everyday problem solving create an incredibly powerful and holistic approach to life. When you combine a compassion based ethics with critical thinking and a strong feeling of personal responsibility it changes how you approach life, the universe and everything. While the shift in your thinking will be subtle the implications for how you live your life and relate to other people will be dramatic.

Humanism has arisen in every culture and in every time. Some form of the Humanist approach has always been taught and most of the major teachers and philosophers throughout history were either Humanists or Humanistic in their teachings.

Humanism is such a powerful approach that most life coaches and motivational speakers teach some aspect of the philosophy. The problem is that most of these coaches and teachers are teaching Humanism lite. You don’t get the full impact of the teachings because they omit key elements that make the approach actually work.

Humanist Learning Systems courses are taught by actual Humanists. People who have a full understanding of the philosophy and who can help you better understand how the various aspects of what you have already been taught work together as an integrated whole so that you can better apply your learning and improve your outcomes.

Do I need to be a Humanist to take these courses?

The Humanistic approach taught here is accessible to anyone of any background. Our ethics are common human ethics and the techniques we use to solve problems will benefit everyone. Most people find this approach to be empowering, enlightening and consistent with their core values.  To get a better idea of the approach taught, check out our blog at:  http://humanisthappiness.blogspot.com/

The only part of the philosophy that gives people pause is Humanism’s rejection of supernaturalism as a problem solving technique. This is part of our commitment to reality based problem solving which we feel yields more effective results than the alternatives.

What Humanist Learning System courses will do is teach you how to be more fully compassionate and loving in your daily life and in your relationships with others. These courses will teach you the practical skills required to think better about problem solving in general and specifically how to think more critically, realistically and effectively about the problems you face. And finally, these courses will help you learn how to apply your core values, love and compassion, into your decision making so you can feel good about yourself and how you are more fully able to live your values.

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Being Human in the Classroom

Developing a Good Mind

Proposition 1. learning should nearly always be satisfying—and often exhilarating., proposition 2. making meaning of the world around us is central to learning., proposition 3. working hard and working wisely are the secrets to mastery., proposition 4. all humans have the ability to be creative., developing a good heart, proposition 1. kindness is the air and water that humans need to thrive., proposition 2. we need to decide what we stand for and who we want to be., proposition 3. we must strive to be good members of the human family., proposition 4. we make our own memories., strong lives and strong resumés.

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Crossing the principle–practice gap in AI ethics with ethical problem-solving

• Original Research
• Open access
• Published: 15 April 2024

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• Nicholas Kluge Corrêa   ORCID: orcid.org/0000-0002-5633-6094 1 , 4 ,
• James William Santos   ORCID: orcid.org/0000-0002-9806-1172 4 , 5 ,
• Camila Galvão   ORCID: orcid.org/0000-0002-4814-4164 2 , 4 ,
• Marcelo Pasetti   ORCID: orcid.org/0000-0003-1993-1422 2 , 4 ,
• Dieine Schiavon   ORCID: orcid.org/0000-0001-8090-2386 2 , 4 ,
• Faizah Naqvi   ORCID: orcid.org/0009-0003-1225-0824 3 ,
• Robayet Hossain   ORCID: orcid.org/0000-0003-0511-3285 3 &
• Nythamar De Oliveira   ORCID: orcid.org/0000-0001-9241-1031 2 , 4  

The past years have presented a surge in (AI) development, fueled by breakthroughs in deep learning, increased computational power, and substantial investments in the field. Given the generative capabilities of more recent AI systems, the era of large-scale AI models has transformed various domains that intersect our daily lives. However, this progress raises concerns about the balance between technological advancement, ethical considerations, safety measures, and financial interests. Moreover, using such systems in sensitive areas amplifies our general ethical awareness, prompting a re-emergence of debates on governance, regulation, and human values. However, amidst this landscape, how to bridge the principle–practice gap separating ethical discourse from the technical side of AI development remains an open problem. In response to this challenge, the present work proposes a framework to help shorten this gap: ethical problem-solving (EPS). EPS is a methodology promoting responsible, human-centric, and value-oriented AI development. The framework’s core resides in translating principles into practical implementations using impact assessment surveys and a differential recommendation methodology. We utilize EPS as a blueprint to propose the implementation of an Ethics as a Service Platform , currently available as a simple demonstration. We released all framework components openly and with a permissive license, hoping the community would adopt and extend our efforts into other contexts. Available in the following URL https://nkluge-correa.github.io/ethical-problem-solving/ .

Avoid common mistakes on your manuscript.

1 Introduction

The late 2010s, especially after the beginning of the deep learning revolution [ 1 , 2 ], marked a rising interest in AI research, underlined by an exponential increase in the academic work related to the field [ 3 , 4 , 5 ]. The confluence of technical advancements (i.e., breakthroughs in deep learning and increased computational power), data availability (i.e., the proliferation of data through social media, smartphones, and IoT devices), and massive investments (i.e., governments and private companies began investing heavily in AI R &D) played a crucial part in the expansion of the field [ 6 , 7 , 8 ]. In the past few years (2022–2023), AI has entered an era where organizations release large-scale foundation models every few months [ 4 ], with systems like GPT-4 [ 9 ], Llama 2 [ 10 ], Gemini [ 11 ], Whisper [ 12 ], CLIP [ 13 ], among many others, becoming the basis of many modern AI applications. The capabilities of such models, ranging from human-like text generation and analysis to image synthesis and unprecedented speech recognition, have revolutionized the public consciousness of AI and transformed how we interact with technology. Footnote 1

This accelerated progress comes with its own set of challenges. Notably, academia released the majority of state-of-the-art machine learning models until 2014. Since then, the market-driven industry has taken over [ 3 , 4 ]. Big tech companies are the current major players in the research and development of AI applications. This shift leads us to question the balance between ethical considerations, safety measures, technological progress, and revenue shares. In other words, prioritization of revenue and “progress in the name of progress” may undercut ethical and safety concerns. Meanwhile, the use of AI systems in sensitive areas, such as healthcare [ 15 , 16 ], banking services [ 17 ], public safety [ 18 , 19 ], among others [ 20 , 21 , 22 ] prompted a re-emergence of the debate surrounding the ethical issues related to the use, development, and governance of these systems and technologies in general [ 23 , 24 , 25 , 26 ].

The AI safety field of research emerges as one possible solution to this unprecedented expansion. AI safety focuses on approaches for developing AI beneficial to humanity and alerts us to the unforeseen consequences of AI systems untied to human values [ 27 , 28 , 29 , 30 ]. At the same time, the growth of the research field in terms of regulation and governance demonstrates an apparent broad consensus on the human values (principles) relevant to AI development and the necessity of enforceable rules and guidelines to apply these values [ 3 , 31 , 32 , 33 , 34 ]. Under this scenario, events like the “Pause Giant AI Experiments open letter” [ 35 ], among others [ 36 , 37 ], are a symptom that even the industry recognizes that the unrestrained AI development impacts will not be positive or manageable [ 30 ]. With that realization, the outcry for regulatory input in the AI development industry grew stronger [ 38 , 39 , 40 ]. Ultimately, the debate on whether consensus on ethical principles exists, how to translate principles to practices, and the interests underlying the push for creating normative guidelines presents critical unanswered questions around AI research [ 41 ].

This work seeks to tackle a critical point within these aforementioned unanswered questions. The principle–practice gap (i.e., translating principles to practice [ 42 ]) presents the sociotechnical challenge of managing the expectations of those who seek a magical normative formula and working with developers to foster design ingrained with ethical principles. With this in mind, we present ethical problem-solving (EPS), a method to aid developers and other professionals responsible for creating autonomous systems (Fig.  1 ).

EPS seeks to bridge the principle-practice gap between principles and practical implementations, giving developers tools to use in the development cycle of an AI system. The general workflow of this method consists of an evaluation (Impact Assessment) and a Recommendation stage, structured in a WHY, SHOULD, and HOW format

Our framework’s core resides in translating abstract principles into practical implementations through impact assessment and a differential recommendation methodology. The EPS builds and improves upon similar works, where proposals are usually limited to paper or worksheet frameworks, while also presenting novel contributions: an implementation of EPS tailored to Brazil’s context. In the following sections, we will explore the challenges faced in ethical AI development, the path toward the EPS, its process, and resources. We released all components of our current implementation openly and with a permissive license, hoping the community can adopt and extend our efforts into other contexts. Footnote 2

2 Related works

This section provides an overview of practical frameworks for AI ethics, specifically emphasizing approaches and methods that help translate ethical principles into developmental insight.

According to Taddeo and Floridi [ 42 ], creating methods to anticipate ethical risks and opportunities and prevent unwanted consequences is the crucial aspect of what the authors call translational ethics. One significant issue for translational ethics is the definition of the appropriate set of fundamental ethical principles to guide the creation, regulation, and application of AI to benefit and respect individuals and societies. Another is the formulation of foresight methodologies to indicate ethical risks and opportunities and prevent unwanted consequences (e.g., impact assessments, stakeholder engagements, and governance frameworks). In the context of AI ethics, translational ethics aims towards concrete and applicable practices that realize broadly accepted ethical principles. In other words, the translation of theoretical conclusions into adequate practice [ 43 ]. However, the authors do not present a particular foresight methodology in the translational framework proposed by Taddeo and Floridi. Nevertheless, they have an optimistic view towards a global convergence of ethical principles that could bring regulatory advancements, as shown in other fields of applied ethics.

Another approach that aims to assess the ethical aspects of AI systems is the VCIO-based (Values, Criteria, Indicators, and Observables) description of systems for AI trustworthiness characterization [ 44 ]. First introduced by the AI Ethics Impact Group in the “From Principles to Practice—An Interdisciplinary Framework to Operationalize AI Ethics” [ 45 ], the VCIO approach identifies core values, defines specific criteria, establishes measurable indicators, and utilizes observable evidence to measure the alignment of AI systems according to the selected values (i.e., transparency, accountability, privacy, justice, reliability, and environmental sustainability), which steam from a meta-analysis of relevant publications. The VCIO approach culminates in an AI Ethics Label. The label offers a rating for each value assessment proposed by the VCIO approach and later communicates an AI system’s ethical score. However, no prerequisites exist to build a label based on the VCIO score. Companies, users, or government bodies can set the requirements for a minimum level of risk within this framework. Moreover, the following publication tied to the VCIO approach [ 44 ] clearly states that the VCIO’s description/evaluation is independent of the risk posed by the technology under consideration and does not define any minimum requirements. It merely describes compliance with the specified values.

Even though VCIO is a valid contribution to the field, its limitations are also apparent in its description. First, the source of the values stated in the papers and the parameters of the meta-analysis that originated them are unclear. Despite being well-known principles in the AI ethics literature [ 31 , 32 , 33 , 34 ], we argue that not knowing how they are defined or uncovered represents a weak spot in the underlying methodology of the VCIO framework. Second, the self-imposing character of the risk evaluation requirements can be an issue and potentially undermine the effect of the approach. If there is no objective measure of the potential risks and impacts on ethical principles, the whole project of applying principles to practice may become moot.

Another approach that instrumentalizes ethical principles into palpable development tools is the Google People AI Research Guidebook [ 46 ], developed by PAIR’s (People + AI Research) multidisciplinary team. The guidebook aims to empower researchers and developers to create AI systems that are fair, inclusive, and unbiased. The guidebook provides six chapters and their respective worksheets that acknowledge the potential risks associated with AI development and emphasize the importance of addressing bias, fairness, interpretability, privacy, and security. The guidebook encourages researchers to adopt a multidisciplinary approach incorporating insights from diverse fields. The worksheets provide strategies for understanding and mitigating bias, creating interpretable AI models, implementing privacy-preserving techniques, and promoting responsible data-handling practices.

PAIR’s guidebook represented a commendable step towards building AI systems that are ethically sound, unbiased, and beneficial to all. Nevertheless, the guidebook and respective worksheets do not present a standard to evaluate whether there is progress in the development. Users of the methodology are left free to gauge their successes and failures, making the whole approach tied to the expectations of the user, who is also the evaluator. Also, the worksheets themselves do not present an approachable user interface.

Other examples of methods created to deal with the same issues previously mentioned works sought to tackle include:

The Digital Catapult AI Ethics Framework [ 47 ] is a set of seven principles and corresponding questions to ensure AI technologies’ responsible and ethical development. The framework advises the developer to consider the principles and their questions. The Digital Catapult underlines that the objective of the questions is to highlight the various scenarios in which ethical principles may apply to the project. Nevertheless, just like in the VCIO methodology [ 44 , 45 ], their method needs to clarify the constitution of the set of relevant values. Meanwhile, the voluntary aspects of the approach rely on the awareness developers must have regarding AI’s potential risks and ethical blank spots.

Microsoft’s AI Ethical Framework [ 48 ] is a set of principles and guidelines designed to ensure AI’s responsible and ethical use. The framework encompasses six principles that should guide AI development and use: fairness, privacy and security, transparency, trust and security, inclusion, and accountability. Each (group of) principle(s) sets its goals and practical measures, bringing back the idea of pragmatizing ethical principles. By adhering to these principles, Microsoft aims to contribute to positive societal impact while mitigating potential risks associated with AI technologies. While the source of the ethical principles remains undisclosed in the paper (a recurring theme in the literature related to this work), the tools meant to achieve said principles are underrepresented, leaving much of the heavy lifting to the developers themselves.

Morley et al. [ 49 ] investigation also revolves around the gap between AI ethics principles, practical implementations, and evaluation of the existing translational tools and methods. The authors propose an assistance method for AI development akin to a Platform as a Service (PaaS). PaaS represents a set-up where core infrastructure, such as operating systems, computational hardware, and storage, is provided to enable developers to create custom software or applications expeditiously. Meanwhile, Ethics as a Service (EaaS) seeks to contextually build an ethical approach with shared responsibilities, where the EaaS provides the infrastructure needed for moral development. In their work, Morley et al. mentions several components that should be a part of this kind of platform, for example, an independent multi-disciplinary ethics board, a collaboratively developed ethical code, and the AI practitioners themselves, among others. EaaS as an idea has the advantage of being relatable to modern tech companies, where organizations subsidize much of their work to specialized third parties. Also, having an ethical evaluation performed by a third party has merits. However, the actual operation of the EaaS framework and the content of the service provided are still ongoing research for the authors.

Baker-Brunnbauer [ 50 ] proposes the Trustworthy Artificial Intelligence Implementation (TAII) Framework, a management guideline for implementing trusted AI systems in enterprises. The framework contains several steps, starting with an overview of the system to address the company’s values and business model. Then, the focus shifts to the definition and documentation of the stakeholders and the exact regulations and standards in play. The assessment of risks and compliance with the common good follows. Ultimately, the framework should generate ethical principles suitable for translation into practical implementations, while executing and certifying the results should be the last step. However, two vulnerabilities of the TAII approach are its self-imposing nature and the many steps involved in the framework. Given these flaws, the iteration of the many measures proposed could leave evaluators blind to the weaknesses of their creation.

As our last mention, we cite the framework created by Ciobanu and Mesnita [ 51 ] for implementing ethical AI in the industry. The proposed framework comprises AI Embedded Ethics by Design (AI EED) and AI Desired State Configuration (AI DSC). The AI EED stage is where the developer can train its model to address the specific ethical challenges of a particular AI application. Meanwhile, the developer or the consumer can define the relevant AI principles using the VCIO approach as the normative source. The AI DSC stage focuses on actively managing the AI system post-implementation through constant user feedback. However, it is unclear how the framework operationalizes its stages, except for the VCIO approach, which also has shortcomings. Also, the framework relies heavily on the interest and acute awareness of the general public to provide feedback on an end-to-end process where the AI system is adapted to the contextual reality where it is implemented.

As shown above, we can see many efforts to incorporate ethics in AI development and deployment. Many of these attempts are still extra-empirical and subject to improvement, modification, and actual deployment. Also, these attempts further justify our introduction’s main point: the imperative necessity to anticipate and mitigate ethical risks in AI system development.

To build upon the work mentioned above, in the following sections, we propose a framework (EPS) that, we argue, is ethically and theoretically grounded, simple, practical, and not self-imposing.

3 Methodology

From the gaps in previous proposals and grounded in our critical analyses of the field of AI ethics, the EPS presents:

A set of assessment surveys aimed at helping evaluators estimate the possible impacts of a given AI application.

An evaluation matrix to classify an AI application’s impact level according to AI principles grounded in empirical research.

A recommendation approach customized to each impact level. These recommendations provide practical suggestions for enhancing the ethical standards of the evaluated system or application.

Before diving into the implementational aspects of our method, let us first revise the philosophical roots of ethical problem-solving.

3.1 Theoretical grounding

The EPS is grounded on Critical Theory’s social diagnosis methods and emancipatory goals, particularly Rahel Jaeggi’s “Critique of Forms of Life”. [ 52 ]. That said, this work’s approach to normativity is not focused on the more traditional aspects of normative theory (i.e., how to judge a particular action as moral and which parameters to use) but is aligned with the notion that normativity is created and reproduced through social practices as an ongoing process. Hence, Jaeggi’s approach fits with normativity as an integral part of sociality, which cannot be dissociated from it to judge what is right or good. This argument is in line with her approach to immanent critique, and it resonates with a few authors who methodologically sustain immanent criticism as the path to avoid universalistic or relativistic tendencies [ 53 , 54 , 55 , 56 ]. However, what differentiates Jaeggi’s approach from others is her conceptualization of forms of life, her normative recuperation of Hegelian theory, her problem-solving approach, and the open practical questions it leaves us with.

Jaeggi draws on the concept of “forms of Life” introduced by Ludwig Wittgenstein [ 57 ], which broadly refers to how individuals and communities organize their activities and ways of understanding the world. Jaeggi reformulates this concept to analyze the social and cultural structures that shape human existence, examining how they might limit human flourishing, autonomy, and self-realization. Jaeggi understands forms of life as clusters of social practices addressing historically contextualized and normatively defined problems. Ultimately, what shapes human existence is how exactly these problems are solved within a form of life.

Broadly speaking, the practices that constitute a form of life are connected in practical-functional ways. Some have a tangible sense of how functionally interdependent they are, such as agricultural practices required for urban consumption. In contrast, others do not, such as playing with children as an essential part of parenthood. In this sense, practices bring their interpretation as something (descriptive) and the functional assignment as being good for something (evaluative) in correlation with each other. Jaeggi thus understands forms of life as being geared to solve problems because their description already carries a meshed functional and ethical perspective. In other words, forms of life always entail an inherent evaluation, excluding pure functionality in human activities. The critical theory of technology, also known as Science and Technology Studies (STS), sustains a similar position that technology is value-laden like other social realities that frame our everyday existence [ 58 , 59 ]. However, the argument of technology being permeated by values or biases in its inception only partially resonates with the proposition of this work. It is Jaeggi’s proposal of the value-laden argument setting up a normative foundation and a problem-solving approach that fits the present endeavor.

Jaeggi asserts that the normative dimensions of forms of life are not static but rather rooted in a triangular relationship involving the current empirical state (is), normative claims (ought), and changing objective conditions. The normative claims reflect the expectation of particular manifestations of social practices as they developed historically, and the current empirical state reflects the actual state of social practices against the expectations and facing objective conditions. This continuous process of dynamic normativity can be illustrated by the development of the rural-feudal extended family into the bourgeois nuclear family due to changed socioeconomic (objective) conditions demanding changes in normative expectations. Further developments, ranging from patchwork families to polyamorous relationships, could also be understood as reactions to the new objective conditions now posed in turn, but not solved, by the bourgeois family [ 52 ].

To address the unsolved issues of social practices and ever-changing objective conditions, Jaeggi proposes problem-solving in the form of a hermeneutic anticipation (i.e., a recommendation) of an assumed solution (i.e., of a desirable goal) and the validity of such a recommendation can only be determined after addressing the identified problems. As we can see, Jaeggi’s approach foreshadows a dynamic normativity that renews itself through iteration without necessarily implying progress from the outset, considering that the success or failure of the recommendation determines the evaluation of the addressed problem. Footnote 3 Jaeggi’s proposal also raised criticisms regarding her conceptualization of forms of life, which was deemed exceedingly vague [ 70 ]. Also, despite Jaeggi’s push to include some notion of social reality in her theoretical proposition, it seems that Jaeggi needed to go further as the approach lacks a clear connection with sociality [ 71 , 72 ]. Although Jaeggi’s work does open practical avenues, much of the work toward changing practices is left out of her proposal.

At this point comes the inspiration for the EPS; at first glance, it might seem striking that there is a slight difference in scope between Jaeggi’s theoretically proposed criticism for societies and the EPS, which seeks to bridge values toward actionable practices of ethical AI development. It is worth underlining that the EPS is not attempting an overarching criticism of technology. However, it can still take advantage of a problem-solving approach that tackles a complex conceptualization and responsively grasps normativity. The EPS puts into practice the problem-solving process theoretically proposed on a different scale but with a complex subject matter and ethical stakes nonetheless. There is no doubt that AI systems involve ensembles of directives to solve diverse issues and that much of the inner workings can be elusive to our current understanding of the subject, not unlike the elusive character of our social practices and the historical background that supports them.

Nevertheless, despite the elusive character of the subject, there is an apparent demand for normalization or, at least, to understand how suitable norms should arise. To this point, the EPS utilizes the dynamic normative approach to test Jaeggi’s theoretical proposal further. The EPS gauges the empirical state of systems (through survey assessment) to trace if their normative assignments are aligned with the ever-changing landscape of AI ethics (considering the state-of-the-art in the field). If there are discrepancies, then problem-solving takes over to align the system. The EPS shines on problem-solving because it enacts something only theorized by Jaeggi; it enables a clear connection between dysfunctionality and its normative assignment to reframe the current state of an AI system with the normative expectations of the field of AI ethics through its practical recommendations. Ultimately, the EPS adaptation of Jaeggi’s dynamic normativity and problem-solving approach transforms the principle–practice gap into an ongoing task open to correction and responsive to its surrounding ethical field.

3.2 Finding values: a descriptive analysis of the field

Much like the previous works mentioned [ 45 , 46 , 47 , 48 ], we embarked on the essential task of surveying the landscape of AI ethics to identify its relevant values. However, one factor that distinguishes our framework from the prevailing body of literature rests in the work of descriptive ethics that preceded the development of EPS, where we rooted the principiological foundations of our framework through a descriptive analysis of how the field defines, instrumentalizes, and proposes AI principles worldwide. This descriptive work is entitled Worldwide AI Ethics (WAIE) [ 3 ].

For starters, WAIE draws inspiration from earlier meta-analytical works [ 31 , 32 , 33 , 73 , 74 ] and meticulously surveys a wide range of ethical guidelines related to AI development and governance through a massive effort of descriptive ethics implemented as a data science venture. In it, 200 documents are thoroughly analyzed, including private company governance policies, academic statements, governmental and non-governmental recommendations, and other ethical guidelines published by various stakeholders in over 30 countries spanning six continents. As a result, WAIE identified 17 resonating principles prevalent in the policies and guidelines of its dataset, which now provide the principles we utilize as the basis for the succeeding stages of the EPS, from the assessments to the recommendations.

Besides the fact that EPS stems from our own meta-analysis of the field, we argue that our principiological foundation differs in the following ways from other works:

WAIE uses a worldwide sample of 200 documents, a more diverse representation of global ethical discourse around AI than previous studies.

WAIE delivers its information in a data visualization way that is interactive and searchable and allows the study of correlations.

WAIE is granular, presenting a series of typologies that increase the insight users can gain.

WAIE is open source, allowing users to replicate and extend our results.

By employing the WAIE review to sustain the EPS methodology, we offer a more nuanced and empirically substantiated perspective on the ethical underpinnings of artificial intelligence, thereby enhancing the depth and rigor of our axiological basis. Footnote 4

However, while the utilization of WAIE has undoubtedly provided a valuable foundation for descriptive, and now normative, ethics in AI, we must emphasize the significance of augmenting our value analyses with a critical evaluation of the risks associated with recently released AI models [ 9 , 10 , 10 , 12 , 13 , 75 , 76 ]. As already stated by Bengio et al. [ 30 ], the field’s dynamic and rapidly evolving landscape necessitates constant vigilance in assessing emerging technologies’ potential pitfalls and challenges (e.g., disinformation, algorithmic discrimination, environmental impacts, social engineering, technological unemployment, intellectual fraud, etc.). Hence, we augmented the development of EPS with a risk analysis of large-scale models released in the last 5 years [ 77 ]. Again, we released all materials tied to this analysis as an open and reproducible project. Footnote 5

By incorporating a critical evaluation of values and known risks, we not only provide a more holistic perspective on AI ethics but also equip stakeholders with a timely understanding of the complex ethical considerations surrounding the deployment of AI systems. This integrated approach ensures that our work remains forward-looking and responsive to the ever-changing landscape in the AI field. These are all fundamental aspects for implementing our method, which, in the first instance, is supported by extensive descriptive work. An empirical-descriptive grounding is paramount to any attempt to pragmatize ethics.

However, this descriptive work only serves as a starting point. As already pointed out by Whittlestone et al. [ 78 ] in their critique of the proliferation of AI principles, we must be ready to realize that, by themselves, principles and risks are insufficient to guide ethics in practice. Hence, now that we have made clear the philosophical, axiological, and descriptive roots of our work, we showcase how we translated these into a practical framework for AI development in the following sections.

3.3 Defining risk and impact with algorithmic impact assessment

The first step in the EPS methodology is to gauge the state of a particular system via an impact assessment survey. Our decision to utilize an impact assessment approach comes from the bourgeoning landscape of legislative efforts worldwide currently focused on this topic (i.e., European Union [ 79 ], Brazil [ 80 ], the United States of America [ 81 , 82 ] [ 83 ], several African states [ 84 , 85 , 86 ], Australia [ 87 ], Argentina [ 88 ], Egypt [ 89 ], Japan [ 90 ], Israel [ 91 ], Estonia [ 92 ], Peru [ 93 ], China [ 94 ], Russia [ 95 ], United Kingdom [ 96 ], Canada [ 97 ], among many others). Whether still in production or already enacted, the realization that governments should legally regulate AI is trending toward unanimity. For example, Brazil (the context where EPS came to be) does not have a bill regulating AI systems specifically. However, several bills to govern such technologies are currently the subject of debate in the National Congress. Nevertheless, from our analysis of these aforementioned regulatory efforts, we argue that two main trends are evident:

Determining the fundamental ethical principles to be protected (which we have achieved through the WAIE review).

A risk-based governance approach toward autonomous systems.

For disambiguation purposes, a risk-based governance approach implies a differential treatment concerning AI systems, i.e., different types of systems demand differential treatment pertaining to the risks they pose. For example, a spam filter would not require the same level of auditing and ethical concern as an autonomous vehicle.

In the EPS framework, we argue that the concepts of risk and impact concerning AI ethics differ in the ex-ante and ex-post relationships. Risk refers to the likelihood of negative consequences arising from the deployment and use of artificial intelligence systems, assessing the potential harm that could result from a particular AI application (ex-ante). On the other hand, impact refers to the magnitude and significance of the actual damage or benefit that occurs when these risks materialize or are mitigated, considering the real-world effects of AI systems on individuals, society, and the environment (ex-post). Regardless of their differences, both concepts are related to the impact these technologies can have on the wild.

Even though both of these terms are used interchangeably in many situations, we choose to use the term impact, aiming to encompass the potential problems (risks) and the actual consequences of harmful AI technologies, including their ethical, social, legal, and economic implications. At the same time, the term “impact” assessment is already accepted and used by the literature [ 98 , 99 , 100 ]. We argue that choosing the term “risk” assessment could entail only a preemptive approach toward assessing AI systems’ negative impacts while introducing less-used terminology. Finally, we also point out that many of the known negative impacts of AI systems are currently documented in the form of “impacts” in many publications that present ethical assessments [ 101 , 102 , 103 , 104 , 105 , 106 ].

Another important aspect related to the development of impact assessment methods is that the impact of AI technologies can vary significantly depending on the cultural, social, political, and economic contexts. For example, concerns for the indigenous population must be considered sensitive topics in contexts such as countries that were former subjects of colonial rule. These topics are not quite paramount in the global north. An AI system that is thoughtfully designed concerning local culture, laws, socioeconomic conditions, and ethics is more likely to succeed and less likely to cause harm in our diverse global landscape [ 107 , 108 ]. Hence, accounting for context is a critical step in the EPS framework to ensure a context-sensitive evaluation. As our theoretical grounding session stated, context is paramount to tracing and addressing the normative assignments in our daily practices. In our results section, we will showcase how we executed this in our implementation.

Finally, in our conception, the evaluation stage of any framework entailed in auditing AI systems should not be fully automated. The sole purpose of an evaluation should be to aid and inform a team of human evaluators, e.g., an ethics board, that can use such information to make a normative decision (e.g., a medical ethics board). In other words, we do not agree that ethical evaluations concerning human issues should be a matter of non-human deliberation. The development of this kind of evaluation board is beyond the scope of this study. Our only input is that such a group should be formed in the most interdisciplinary way possible, having representatives of all areas concerning AI ethics, e.g., Computer Science, Philosophy, Law, etc, as already suggested by other works [ 44 , 45 , 46 , 109 , 110 ]. Also, it is essential to note that in our conception, such evaluation should be administered by third parties or sources outside of the developmental cycle of the technologies under consideration, making the EPS framework not a self-evaluation method but a process that requires the collective engagement of AI developers and auditing parties.

3.4 WHY–SHOULD–HOW: a three-step approach to ethical problem-solving

The EPS framework acts as the bridge between the recognition of AI system dysfunctionality and its normative assignment. As previously mentioned, through EPS, it becomes possible to identify, assess, and understand the ethical implications of an AI system. Moreover, EPS provides practical recommendations to reframe an AI system’s actual condition in alignment with the expectations of the AI ethics community. Therefore, problem-solving becomes the act of providing recommendations informed by the current state of the AI system and the normative assignments demanded from within the system and by the field of AI ethics. The WHY–SHOULD–HOW methodology appeared as a palpable and direct form to underline the relevancy of ethical principles in AI development while stating the normative standards and offering comprehensive recommendations to address the issues. This three-step process attempts to grasp the essential features of shortening the principle–practice gap in AI development.

First, the WHY component serves as the foundation, demonstrating the relevance of the AI principles to the specific issue at hand. It encourages practitioners and organizations to acknowledge why they should uphold particular values and the consequences of the opposite. This step is crucial as it sets the stage for a deeper understanding of AI applications’ implications and broader societal and ethical impacts. Also, the WHY step is aligned with the epistemological claim that informed developers are better than uninformed ones, which revolves around the fundamental idea that ethical knowledge and understanding are essential to technological development. In this context, “informed” developers would better understand the principles, best practices, and technologies relevant to their field, while “uninformed” developers may lack this knowledge. In other words, explaining why something is suitable is the first step in any approach that seeks to promote moral reasoning convincingly. Otherwise, starting with an imperative claim may seem authoritative.

While the WHY step represents the foundation and contextualization of principles, SHOULD and HOW are its pragmatization. The last two stages are associated with different levels of impact, inferred at the EPS assessment stage (low, intermediate, and high). Footnote 6 The SHOULD aspect outlines the necessary steps to tackle ethical problems. By necessary, we mean that the measures indicated in this step represent the axiological content of each principle in the framework and are integral to developing an ethically attuned AI system. We can also define the SHOULD stage as an implementation of normative ethics in an applied format, where besides defining explicit “oughts,” we stipulate criteria to help users and evaluators assess the compliance of a given system. This step traces a causal relationship between principles and observables, taking the VCIO approach as inspiration [ 44 , 45 ].

However, the EPS aims to go beyond the normative guidance, presenting the how-to-do, i.e., the practical step. Hence, the HOW component offers valuable tools and strategies to implement the ethical recommendations in the SHOULD stage. In short, it equips developers, researchers, and organizations with the means to put ethical principles into practice. The scarcity of practical tools to address ethical matters within AI is a significant and concerning gap in AI ethics [ 3 , 32 , 111 , 112 ]. Most of the literature that brings ethics to the development of applications does so through its descriptions of principles and extensive flowcharts of how the AI development process should be, failing to provide the practical support to address ethical problems or achieve the principles it underlines. In the meantime, developers often face unique and context-specific dilemmas, and without practical guidance, they may resort to ad-hoc solutions or bypass critical considerations altogether. Without available tools to guide developers or the know-how of how to use them, it is no surprise that there is a lack of standardized ethical practices in AI development [ 113 , 114 ], resulting in blank spots and inconsistencies across the life cycle of AI projects.

Thus, given the deficits mentioned above, the normative step alone is insufficient to cross the principle–practice gap, making the absence of the HOW to step a clear blank spot in other works that our framework seeks to surpass. Ultimately, the WHY–SHOULD–HOW approach culminates in an educational step, acknowledging that responsible AI development is far from standard practice in the curriculum of many STEM-related fields that sprout most AI developers. In our envisioned form, this whole process aims to integrate professionals from humane sciences to STEM-field areas, and vice-versa, bringing developmental focus to ethics and developmental mindset to ethical considerations. Hence, if we suppose, taking a Virtue Ethics stance, that moral behavior can only stem from practice, our proposed framework allows practitioners to develop their virtues through training, which is why we implemented the HOW step as an “educational” step, besides a practical one.

In the following section, we delve into the heart of our work, presenting an implementation of the ethical problem-solving framework. This implementation offers a blueprint for constituting an EaaS platform to apply our envisioned framework.

In this section, we will show an implementation of the EPS framework. The idea of Ethics as a Service guided the creation of this implementation. Following the attempt of Morley et al. [ 49 ], this EaaS implementation would provide the infrastructure for ethical AI development akin to what a Platform as a Service offers, i.e., a platform where developers can submit their systems to ethical auditing.

As mentioned before, the EaaS idea has the advantage of being relatable to modern companies, where third parties constantly subsidize services and infrastructure that is too costly to maintain. While this may not be the case for large organizations (i.e., organizations that, besides having their own technological infrastructure, also have their own ethics boards), an EaaS may as well be a valid tool for companies that cannot sustain or afford their own AI ethics and safety auditing. Also, we again stress the value of a neutral, third-party auditing platform, regardless of the organization’s size.

The components of our envisioned implementation are:

The EPS framework (questionnaires, evaluation metrics, recommendations, and educational aid).

A platform to apply this methodology.

An ethics board to perform the evaluations.

The subsections below present a step-by-step implementation of the EPS framework as an EaaS platform tailored to the Brazilian context.

4.1 Evaluation stage: algorithmic impact assessment and ethical troubleshoot

The flow of the ethical problem-solving framework begins with a pre-algorithmic impact assessment. The pre-assessment gauges preemptively the realm of impact of a particular system, leading to the actual tools of impact assessment. In other words, this preliminary assessment informs the user what algorithmic impact assessment surveys (AIAs) are required to fulfill the evaluation stage. For example, the user must perform the privacy and data protection AIA if the intended application utilizes personally identifiable information. Hence, after this brief assessment, the user is directed to the next step: the EPS’ algorithmic impact assessment surveys (Fig.  2 )

This flowchart illustrates the evaluation structure of the EPS framework. The dotted lines trace the pre-assessment leading through the algorithmic impact assessment surveys. The straight line represents the indispensable survey throughout the framework: the Ethical Troubleshoot. All survey assessments lead to a human-centered evaluation process

The algorithmic impact assessment surveys consist of questionnaires with pre-defined questions and answers that can be single-choice or multiple-choice (Fig.  3 ). Our current implementation of these covers the following themes: data protection and privacy, protection of children and adolescents, antidiscrimination, and consumer rights. Choosing these areas was a strategic move to gather resources since they are rich in legislative content in the Brazilian context. Even though Brazil still needs specific AI regulations, other legal sources can still be used to determine what is and is not allowed regarding the use and development of AI technologies. This design choice also highlights another important aspect of the EPS framework: the importance of contextual information while developing the evaluation stage of an implementation of our framework. This implementation aims to show stakeholders and researchers how these assessments can be created even if AI is not explicitly regulated.

We developed the questions from the algorithmic impact assessment surveys to infer the level of impact a particular system may have on different ethical principles. Each question’s response could either raise the impact, remain unchanged, or lower it if mitigating measures have been found

The questionnaires for the algorithmic impact assessment surveys entail that each of the questions identifies the AI’s compliance with at least three ethical principles identified by the WAIE review. Hence, each of these generates impact scores relative to these assessed principles. As a result, distinct principles may serve as the basis for each question in each AIA. At the same time, each question’s response could either raise the impact, remain unchanged, or lower it if mitigating measures have been found. In other words, we use objective questions tied to contextually relevant legally binding norms intended to guarantee a good life to infer the impact caused by a technology under consideration. Our current implementation of these impact assessment surveys presents for each question:

Their possible answers.

The scores related to each answer.

The principles impacted by the answers to each question.

Ultimately, these assessments can generate a standardized impact level on each ethical principle evaluated by each AIA. At the same time, the overall cumulative impact of all assessed principles represents the general impact of a system against a specific AIA. For example, in our privacy and data protection AIA, the following principles could be impacted, depending on the answers given by the user: privacy, transparency, and accountability. Hence, the final result of the privacy and data protection AIA presents an individual impact score for each principle and an overall score on the AIA itself (the standardized summation of each evaluated principle):

The algorithmic impact assessment surveys use legally binding standards to deduce the implications of AI systems through an objective lens. However, these questionnaires provide an impact score that cannot address all of the ubiquities attached to the ethical issues that AI systems present. Hence, in our current implementation of the EPS framework, we developed a more qualitative survey to accompany them, entitled Ethical Troubleshoot, aimed at going beyond an objective evaluation.

In short, the Ethical Troubleshoot survey seeks to allow the respondent to divulge how the development of a given AI system or application has been done in a human-centric way, e.g., how the needs of the intended users have been considered. It utilizes a combination of multiple-choice, single-choice, and open-ended questions to gauge the system’s scope, its intended and unintended uses, and its target audience. We argue that a mixture of objective evaluation modes and more qualitative assessment forms can only augment a human-centric ethical evaluation (Fig.  4 ). In other words, this qualitative survey is meant to capture the aspects that the more objective and rigid AIAs could not assess. Our implementation of the Ethical Troubleshoot survey was mainly achieved by reverse engineering the VCIO method [ 44 , 45 ] and the Google-PAIR worksheets [ 46 ].

The EPS framework’s evaluations are designed to help human evaluators assess the level of impact of an AI system. Each evaluated principle has three distinct levels of impact. After being informed by the outputs of the evaluation stage, human evaluators prescribe the particular impact level of an AI system regarding the ethical principles being considered

As already mentioned, the sole purpose of these evaluation surveys is to help inform a team of human evaluators, e.g., an ethics board, that can use such information to make a human-centered evaluation. Footnote 7 The output of this decision is the recommendation stage.

4.2 Recommendation stage: WHY–SHOULD–HOW

After the evaluation stage, the EPS framework requires that human evaluators classify the system under consideration in an impact matrix. The matrix comprises three recommendation levels tailored to each impact level—high, intermediate, and low—and six ethical principles gathered from the WAIE review, i.e., fairness, privacy, transparency, reliability, truthfulness, and sustainability. Hence, each principle has three distinct possible recommendations tailored to specific impact levels, e.g., Sustainability-low, Sustainability-intermediate, and Sustainability-high (Fig.  5 ).

After being moderated and revised by an ethics board (human-centered evaluation), the assessment output is an ethical framing, where the system under consideration is classified with an impact level (high, intermediate, and low) for each of the evaluated principles

The WHY–SHOULD–HOW method is the format in which our approach presents the evaluation’s outcome. First, the WHY step is structured to demonstrate the relevancy of each principle, providing the conceptualization and highlighting paradigmatic cases of deficit implementation in a structure that answers the questions “ What is said principle? ” and “ Why should you care about it? ”. Second, the SHOULD step provides the metric utilized to gauge the level of recommendation regarding the corresponding principle, the level of recommendations indicated for the specific case, and the set of recommendations in a summarized form. Third and finally, the HOW component offers the practical tools and strategies required to implement the recommendations made in the SHOULD stage, i.e., it pragmatizes the normative recommendations of the previous step while also providing how-to instructions on using them (Fig.  6 ).

Each level of recommendation regarding the principles utilized is structured around the WHY–SHOULD–HOW method. This allows the evaluators to make differential recommendations based on each principle’s inferred level of impact. Subsequently, each level of impact presents differential recommendations with tailored tools and practices for that specific impact level

We developed the EPS framework to address the principle–practice gap explicitly. Given that we wished to go beyond simply “pointing to what tools developers can use,” we developed an open-source repository containing a collection of demonstrations of how to use the developmental tools we recommend as part of the EPS [ 116 ]. This repository has many examples of tools and techniques developed to deal with the potential issues of an AI application (e.g., algorithmic discrimination, model opacity, brittleness, etc.), using as test cases some of the most common contemporary AI applications (e.g., computer vision, natural language processing, forecasting, etc.) (Fig.  7 ).

We structured the HOW step around the question, “ How can a developer increase the robustness of a system in regards to a specific ethical principle? ”, laying down step-by-step instructions, toolkits, educational resources, testing, and training procedures, among other resources that can help apply the principles used in the EPS

The effectiveness of a tool or framework often hinges on its ease of use and implementation. For instance, PyTorch’s popularity in deep learning stems from, besides the inherent value of its automatic differentiation engine, its comprehensive documentation, which facilitates widespread adoption by lowering the entry barrier for newcomers to the field, simplifying complex procedures, like neural network engineering and training, to simple how-to-do examples. In the case of the EPS, we try to accomplish the same for the practices related to AI ethics and safety. To better exemplify this, let us describe a hypothetical use of our framework as an EaaS platform:

Hypothetical use case: An organization in Brazil is in the process of developing an AI-empowered product. Before deploying it to its first users, the organization applies the EPS method via an EaaS platform to access ethical and legal compliance. During the evaluation stage, the organization answers the surveys in the best way possible, disclosing all information required, protected by a non-disclosure agreement between both parties. After the evaluation stage, the ethics board working behind the platform receives the results of the product evaluations. This information is also disclosed to the organization since it gives valuable information about the legal compliance of the product under several legislative works. Imbued with the results of the evaluation stage, the ethics board frames the product into the pre-established impact levels, giving rise to a particular set of recommendations (WHY–SHOULD–HOW). The EaaS platform delivers this documentation back to the organization, together with their tailored review. This deliverable presents criteria and tools to improve a product according to the principles under consideration. To further help, these tools are offered in a pedagogical form, i.e., via documented examples of use cases (e.g., how to perform adversarial exploration, evaluate fairness metrics, interpret language models, etc.), to improve their adoption and use. These are presented as step-by-step procedures to improve the organization’s product further.

The workflow of our implementation combines aspects related to ethical principles, legal compliance, and technical implementations, articulating all of them together akin to the “Stronger Together” proposal of Pistilli et al. [ 117 ]. This, we argue, ultimately leads to tightening the principle–practice gap, i.e., from AI to beneficial AI. Meanwhile, the point in which our framework goes beyond past works in shortening the principle–practice gap lies heavily in our pedagogical aspect. Past frameworks almost always give you the normative (the ought) and, more rarely, the practical (the how). Besides giving normative recommendations, our framework seeks to teach developers how to use tools to tackle ethical and safety problems. At the same time, the materials that support our framework are all openly available, making this study one of the first efforts to tailor an AI governance approach to the Brazilian context in an open-source fashion while also allowing for spin-offs tailored to different contexts. Readers can access the source code and all other materials tied to the EPS framework at the following URL https://nkluge-correa.github.io/ethical-problem-solving/ .

5 Limitations and future works

EPS represents a novel attempt to bridge the principle–practice gap between ethical principles and the practical implementation of AI development. However, much work remains.

First, while the framework provides a structured approach to addressing ethical concerns, handling AI’s ever-evolving landscape is a tiring feature we must come to terms with. Many foundations of our approach rest on work that is bound to be outdated. The axiological roots of the EPS framework (the WAIE review), the legislative sources we used to create our impact assessment surveys, our risk assessment catalog, and the practices we recommend and exemplify are all bound to become irrelevant as advances in these fronts occur. Hence, the fast-moving nature of the field requires implementations of our framework to undergo constant recycling; otherwise, we risk falling into uselessness. This fact leads us to the question, “Can humane sciences accompany the accelerated pace of the technological industry?” which, in our opinion, has till now been answered negatively and with pessimism. As mentioned, bridging the principle–practice gap is a continuous problem-solving process, and as Jaeggi points out [ 52 ], problem-solving is a never-ending work. Hence, an obvious future avenue of work involves updating and extending EPS. For example, there is undoubtedly space to expand the legislative contribution in subsequent implementations of the EPS, even more so if this expansion encompasses legislation specifically focused on AI systems (e.g., Generative AI). Also, it remains open the possibility to integrate more general frameworks, like international human rights [ 118 , 119 , 120 ], which are already a part of some impact assessment tools tailored to the assessment of human rights [ 121 ].

Another sensitive point of our framework regarding its evaluation method is its scoring process. In our current implementation, we constrained our impact score scale to a standard range, where answers to questions could maximally impact a given principle with a score of 1 or decrease its impact with a score of − 1. At the same time, we chose to have more ways in which impact scores could be increased rather than decreased, i.e., \(\approx \frac{1}{5}\) of the general score from each AIA produces a reduction in impact, given that the main objective of our evaluations is to assess the lack of ethical compliance. Hence, one issue we face is the feasibility of translating regulatory standards into a cardinal evaluation scale. The problem of intertheoretic comparisons and the gauging of how much “utility” a developmental choice should represent is not trivial, being an area of open research in Moral Philosophy and Metanormativity [ 122 , 123 , 124 , 125 ]. Given that we developed these evaluations to inform a body of experts imbued with making a normative framing, finding ways to present evaluations understandably and unambiguously is crucial. While our approach translates standardized cardinal values to unambiguous impact classes (low, intermediate, and high), other methods might be better suited. Searching for improved ways to perform this translation is an area of study worth pursuing.

Meanwhile, the idea of a human-centric evaluation presents its own problem. This human-centric aspect, which, in the end, comes down to the biased and subjective view of a group of individuals, is one of its weak spots. Like many other forms of evaluation and certification that rely on human oversight, the EPS may also fall short of its promise if its human element is unaligned with the core purpose of the framework. While the idea of an EaaS platform that should always be managed (or at least audited) by an external party, such as a government body or a neutral auditing organization, may help avoid specific perils without a proper normative engine (a.k.a. an evaluation board or an ethics committee), the whole idea of EaaS could deteriorate into Ethics Washing as a Service [ 126 ]. We remain committed to the concept of not automatizing ethics. However, we argue that the success of this type of framework also rests in the question of how to train and educate good ethical boards to perform this crucial role [ 109 , 110 ], which is another avenue for future studies.

This work also explored the limitations of shortening the principle–practice gap with a toolbox mentality. In other words, we encountered several cases where we may need more than mere tools and the knowledge of how to use them to fulfill our higher goals. For example, one can use statistical metrics and other tools to assess the fairness of an AI system and further correct them. However, these do not attack the root cause of inequality that becomes imprinted in our systems and applications [ 127 ]. One can use several methods to protect and obfuscate personally identifiable information during an AI development cycle. Yet, robust privacy can only be achieved through collaboration among governments, private companies, and other stakeholders [ 128 , 129 ]. One can use carbon tracking to offset their footprint and promote sustainable practices. But unfortunately, sustainability in AI ethics cannot be reduced to such mere calculations, given the many other side effects of our technological progress do not have an easy-to-measure metric, e.g., the depletion of our natural resources [ 130 ], the humanitarian costs related to their extraction [ 5 , 131 , 132 ], production of e-waste [ 133 ], etc. All these cases stress that AI ethics has challenges beyond the “lack of implementational techniques” or “knowledge gaps,” which should incentivize works and agendas that use a different approach than the one we utilized in the EPS.

Other limitations that fall outside the scope of our framework but can prevent (or improve) its success are these:

Regulatory supports and incentives: frameworks like the EPS and the development of EaaS platforms can become a future necessity if regulatory bodies make this evaluation a mandatory procedure for AI products above a certain impact level. At the same time, regulatory bodies could adopt frameworks like this and serve it as a certification system. On the other hand, it could also be the case that frameworks like these would only have adoption with regulatory support and, without it, would find no adoption in the industry. Just as in the case of organizations that provide cybersecurity and GDPR compliance services, their adoption is tied to regulations that make compliance necessary for them.

Lack of attention to ethical issues in entrepreneurial environments: as already mentioned by previous studies [ 114 ], entrepreneurial settings, where many modern technologies become commoditized into products, are not environments that usually take ethical concerns too seriously, where these are generally seen as a nuisance or barrier to further progress. Currently, the field necessitates minimizing knowledge asymmetry between all sectors, from humane sciences to STEM fields. To research and develop business and applications. However, there are still many obstacles to this type of collaboration and how to overcome the challenges of interdisciplinary research.

Lack of virtues in AI and software developers: studies have already shown that we might have a Humane Sciences gap in STEM areas [ 113 , 134 ], while questions related to AI ethics and safety are still far from the mainstream in terms of Academic curricula. However, we argue that practices like the ones promoted in our framework could help patch this in the educational development of STEM professionals acting as AI engineers and developers. Regardless, improving the “Humane aspect” of the formation of these professionals could undoubtedly improve their sensibility to the issues dealt with by frameworks like the EPS.

These are research areas that can, directly and indirectly, improve not just the success of this study’s objectives, i.e., shortening the principle–practice gap, but AI Ethics itself. In this landscape, our framework is a blueprint for other researchers to build upon and expand. The entirety of the proposed process gives more than enough space for the many areas related to AI ethics to contribute, like, for example, improving evaluation methods, coming up with recommendations for ethical design choices, creating tools, or teaching developers how to use them. Our objective for the foreseeable future is to fully implement and test the EPS framework as an EaaS platform in Brazil while supporting and updating our open-source repositories. We hope this work and service may provide novel pathways and opportunities to AI ethicists and better general guidance and assistance to the field.

6 Conclusion

In this work, we presented ethical problem-solving, a broad framework pursuing the betterment of AI systems. Our framework employs impact assessment tools and a differential recommendation methodology. The EPS framework differentiates itself from other works by its theoretical grounding, axiological foundation, and operationalization, serving as the blueprints for an EaaS platform that mediates the normative expectations of the field and the reality of AI system development. However, crossing the principle–practice gap in AI development is an ongoing process. Even though many problems remain without immediate technical solutions, efforts like this can help institute a culture of responsible AI practice in a way that can keep pace with the advances in the field. Finally, by opening our work and results, we hope other researchers can easily extend and surpass our work.

Data availability

The authors confirm that all data related to this study is available at the following URL: https://github.com/Nkluge-correa/ethical-problem-solving .

In this study, the term Artificial Intelligence (AI), and more specifically, AI systems and applications, are defined as the products generated by the interdisciplinary field of computer science, cognitive sciences, and engineering that focus on creating machines capable of performing tasks that typically require human intelligence (e.g., natural language processing, problem-solving, pattern recognition, decision-making, forecasting, etc.) [ 14 ].

All materials tied to this study are available in https://nkluge-correa.github.io/ethical-problem-solving/ .

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Acknowledgements

This research was funded by RAIES (Rede de Inteligência Artificial Ética e Segura). RAIES is a project supported by FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico).

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Nicholas Kluge Corrêa

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Brown University, Providence, USA

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Nicholas Kluge Corrêa, James William Santos, Camila Galvão, Marcelo Pasetti, Dieine Schiavon & Nythamar De Oliveira

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This study involved a collaborative effort from a multidisciplinary team. N.K.C. contributed to the development of the methodology, the creation of the demo and related code repositories, and the writing of the article. J.W.S. contributed to the development of the methodology, the documentation of the demo, and the writing of the article. C.G. contributed to the development of the AIAs, to writing the corresponding sections related to the AIAs, and aided in the development of the overall methodology. M.P. contributed to the development of the AIAs, to writing the corresponding sections related to the AIAs, and aided in the development of the overall methodology. D.S. contributed to the development of code repositories, to the writing of the article, and aided in the development of the overall methodology. F.N. contributed to the development of the recommendation approach and aided in the development of the overall methodology. R.H. contributed to the development of the recommendation approach and aided in the development of the overall methodology. N.O. is the project coordinator.

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Correspondence to Nicholas Kluge Corrêa .

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Corrêa, N.K., Santos, J.W., Galvão, C. et al. Crossing the principle–practice gap in AI ethics with ethical problem-solving. AI Ethics (2024). https://doi.org/10.1007/s43681-024-00469-8

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Received : 17 December 2023

Accepted : 07 March 2024

Published : 15 April 2024

DOI : https://doi.org/10.1007/s43681-024-00469-8

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Collaborative Robotics is prioritizing ‘human problem solving’ over humanoid forms

Humanoids have sucked a lot of the air out of the room. It is, after all, a lot easier to generate press for robots that look and move like humans. Ultimately, however, both the efficacy and scalability of such designs have yet to be proven out. For a while now, Collaborative Robotics founder Brad Porter has eschewed robots that look like people. Machines that can potentially reason like people, however, is another thing entirely.

As the two-year-old startup’s name implies, Collaborative Robotics (Cobot for short) is interested in the ways in which humans and robots will collaborate, moving forward. The company has yet to unveil its system, though last year, Porter told me that the “novel cobot” system is neither humanoid nor a mobile manipulator mounted to the back of an autonomous mobile robot (AMR).

The system has, however, begun to be deployed in select sites.

“Getting our first robots in the field earlier this year, coupled with today’s investment, are major milestones as we bring cobots with human-level capability into the industries of today,” Porter says. “We see a virtuous cycle where more robots in the field lead to improved AI and a more cost-effective supply chain.”

Further deployment will be helped along by a fresh $100 million Series B, led by General Catalyst and featuring Bison Ventures, Industry Ventures and Lux Capital. That brings the Bay Area firm’s total funding up to $140 million. General Catalyst’s Teresa Carlson is also joining the company in an advisory role.

Cobot has the pedigree, as well, with staff that includes former Apple, Meta, Google, Microsoft, NASA and Waymo employees. Porter himself spent more than 13 years at Amazon. When his run with the company ended in summer 2020, he was leading the retail giant’s industrial robotics team.

Amazon became one of the world’s top drivers and consumer of industrial robotics during that time, and the company’s now ubiquitous AMRs stand as a testament to the efficiency of pairing human and robot workers together.

AI will, naturally, be foundational to the company’s promise of “human problem solving,” while the move away from the humanoid form factor is a bid, in part, to reduce the cost of entry for deploying these systems.