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AI-related publications worldwide 2016-2020, by country

Number of artificial intelligence (ai) publications worldwide from 2016 to 2020, by country (in 1,000s).

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2016 to 2020

The source considered publications in the topic category "Computer science, artificial intelligence" in the following document types indexed in Web of Science Core Collection: article, proceedings paper, review, book, and book chapter.

Statistics on " AI in China "

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THE AI INDEX REPORT

Measuring trends in AI

ai iNDEX anNUAL rEPORT

Welcome to the 2024 AI Index Report

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine. The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

TOP TAKEAWAYS

1. a i beats humans on some tasks, but not on all..

AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding. Yet it trails behind on more complex tasks like competition-level mathematics, visual commonsense reasoning and planning.

2. Industry continues to dominate frontier AI research .

In 2023, industry produced 51 notable machine learning models, while academia contributed only 15. There were also 21 notable models resulting from industry-academia collaborations in 2023, a new high.

3. Frontier models get way more expensive .

According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute.

4. The United States leads China, the EU, and the U.K. as the leading source of top AI models.

In 2023, 61 notable AI models originated from U.S.-based institutions, far outpacing the European Union’s 21 and China’s 15.

5. Robust and standardized evaluations for LLM responsibility are seriously lacking.

New research from the AI Index reveals a significant lack of standardization in responsible AI reporting. Leading developers, including OpenAI, Google, and Anthropic, primarily test their models against different responsible AI benchmarks. This practice complicates efforts to systematically compare the risks and limitations of top AI models.

6. Generative AI investment skyrockets.

Despite a decline in overall AI private investment last year, funding for generative AI surged, nearly octupling from 2022 to reach $25.2 billion. Major players in the generative AI space, including OpenAI, Anthropic, Hugging Face, and Inflection, reported substantial fundraising rounds.

7. The data is in: AI makes workers more productive and leads to higher quality work.

In 2023, several studies assessed AI’s impact on labor, suggesting that AI enables workers to complete tasks more quickly and to improve the quality of their output. These studies also demonstrated AI’s potential to bridge the skill gap between low- and high-skilled workers. Still other studies caution that using AI without proper oversight can lead to diminished performance.

8. Scientific progress accelerates even further, thanks to AI.

In 2022, AI began to advance scientific discovery. 2023, however, saw the launch of even more significant science-related AI applications—from AlphaDev, which makes algorithmic sorting more efficient, to GNoME, which facilitates the process of materials discovery.

9. The number of AI regulations in the United States sharply increases.

The number of AI-related regulations in the U.S. has risen significantly in the past year and over the last five years. In 2023, there were 25 AI-related regulations, up from just one in 2016. Last year alone, the total number of AI-related regulations grew by 56.3%.

10. People across the globe are more cognizant of AI’s potential impact—and more nervous.

A survey from Ipsos shows that, over the last year, the proportion of those who think AI will dramatically affect their lives in the next three to five years has increased from 60% to 66%. Moreover, 52% express nervousness toward AI products and services, marking a 13 percentage point rise from 2022. In America, Pew data suggests that 52% of Americans report feeling more concerned than excited about AI, rising from 38% in 2022.

Chapter 1: Research and Development

This chapter studies trends in AI research and development. It begins by examining trends in AI publications and patents, and then examines trends in notable AI systems and foundation models. It concludes by analyzing AI conference attendance and open-source AI software projects.

1. Industry continues to dominate frontier AI research.
2. More foundation models and more open foundation models.
3. Frontier models get way more expensive.
5. The number of AI patents skyrockets.
6. China dominates AI patents.
7. Open-source AI research explodes.
8. The number of AI publications continues to rise.

Chapter 2: Technical Performance

The technical performance section of this year’s AI Index offers a comprehensive overview of AI advancements in 2023. It starts with a high-level overview of AI technical performance, tracing its broad evolution over time. The chapter then examines the current state of a wide range of AI capabilities, including language processing, coding, computer vision (image and video analysis), reasoning, audio processing, autonomous agents, robotics, and reinforcement learning. It also shines a spotlight on notable AI research breakthroughs from the past year, exploring methods for improving LLMs through prompting, optimization, and fine-tuning, and wraps up with an exploration of AI systems’ environmental footprint.

1. AI beats humans on some tasks, but not on all.
2. Here comes multimodal AI.
3. Harder benchmarks emerge.
4. Better AI means better data which means … even better AI.
5. Human evaluation is in.
6. Thanks to LLMs, robots have become more flexible.
7. More technical research in agentic AI.
8. Closed LLMs significantly outperform open ones.

Chapter 3: Responsible AI

AI is increasingly woven into nearly every facet of our lives. This integration is occurring in sectors such as education, finance, and healthcare, where critical decisions are often based on algorithmic insights. This trend promises to bring many advantages; however, it also introduces potential risks. Consequently, in the past year, there has been a significant focus on the responsible development and deployment of AI systems. The AI community has also become more concerned with assessing the impact of AI systems and mitigating risks for those affected. This chapter explores key trends in responsible AI by examining metrics, research, and benchmarks in four key responsible AI areas: privacy and data governance, transparency and explainability, security and safety, and fairness. Given that 4 billion people are expected to vote globally in 2024, this chapter also features a special section on AI and elections and more broadly explores the potential impact of AI on political processes.

1. Robust and standardized evaluations for LLM responsibility are seriously lacking.
2. Political deepfakes are easy to generate and difficult to detect.
3. Researchers discover more complex vulnerabilities in LLMs.
4. Risks from AI are a concern for businesses across the globe.
5. LLMs can output copyrighted material.
6. AI developers score low on transparency, with consequences for research.
7. Extreme AI risks are difficult to analyze.
8. The number of AI incidents continues to rise.
9. ChatGPT is politically biased.

Chapter 4: Economy

The integration of AI into the economy raises many compelling questions. Some predict that AI will drive productivity improvements, but the extent of its impact remains uncertain. A major concern is the potential for massive labor displacement—to what degree will jobs be automated versus augmented by AI? Companies are already utilizing AI in various ways across industries, but some regions of the world are witnessing greater investment inflows into this transformative technology. Moreover, investor interest appears to be gravitating toward specific AI subfields like natural language processing and data management. This chapter examines AI-related economic trends using data from Lightcast, LinkedIn, Quid, McKinsey, Stack Overflow, and the International Federation of Robotics (IFR). It begins by analyzing AI-related occupations, covering labor demand, hiring trends, skill penetration, and talent availability. The chapter then explores corporate investment in AI, introducing a new section focused specifically on generative AI. It further examines corporate adoption of AI, assessing current usage and how developers adopt these technologies. Finally, it assesses AI’s current and projected economic impact and robot installations across various sectors.

1. Generative AI investment skyrockets.
2. Already a leader, the United States pulls even further ahead in AI private investment.
3. Fewer AI jobs, in the United States and across the globe.
4. AI decreases costs and increases revenues.
5. Total AI private investment declines again, while the number of newly funded AI companies increases.
6. AI organizational adoption ticks up.
7. China dominates industrial robotics.
8. Greater diversity in robot installations.
9. The data is in: AI makes workers more productive and leads to higher quality work.
10. Fortune 500 companies start talking a lot about AI, especially generative AI.

Chapter 5: Science and Medicine

This year’s AI Index introduces a new chapter on AI in science and medicine in recognition of AI’s growing role in scientific and medical discovery. It explores 2023’s standout AI-facilitated scientific achievements, including advanced weather forecasting systems like GraphCast and improved material discovery algorithms like GNoME. The chapter also examines medical AI system performance, important 2023 AI-driven medical innovations like SynthSR and ImmunoSEIRA, and trends in the approval of FDA AI-related medical devices.

1. Scientific progress accelerates even further, thanks to AI.
2. AI helps medicine take significant strides forward.
3. Highly knowledgeable medical AI has arrived.
4. The FDA approves more and more AI-related medical devices.

Chapter 6: Education

This chapter examines trends in AI and computer science (CS) education, focusing on who is learning, where they are learning, and how these trends have evolved over time. Amid growing concerns about AI’s impact on education, it also investigates the use of new AI tools like ChatGPT by teachers and students. The analysis begins with an overview of the state of postsecondary CS and AI education in the United States and Canada, based on the Computing Research Association’s annual Taulbee Survey. It then reviews data from Informatics Europe regarding CS education in Europe. This year introduces a new section with data from Studyportals on the global count of AI-related English-language study programs. The chapter wraps up with insights into K–12 CS education in the United States from Code.org and findings from the Walton Foundation survey on ChatGPT’s use in schools.

1. The number of American and Canadian CS bachelor’s graduates continues to rise, new CS master’s graduates stay relatively flat, and PhD graduates modestly grow.
2. The migration of AI PhDs to industry continues at an accelerating pace.
3. Less transition of academic talent from industry to academia.
4. CS education in the United States and Canada becomes less international.
5. More American high school students take CS courses, but access problems remain.
6. AI-related degree programs are on the rise internationally.
7. The United Kingdom and Germany lead in European informatics, CS, CE, and IT graduate production.

Chapter 7: Policy and Governance

AI’s increasing capabilities have captured policymakers’ attention. Over the past year, several nations and political bodies, such as the United States and the European Union, have enacted significant AI-related policies. The proliferation of these policies reflect policymakers’ growing awareness of the need to regulate AI and improve their respective countries’ ability to capitalize on its transformative potential. This chapter begins examining global AI governance starting with a timeline of significant AI policymaking events in 2023. It then analyzes global and U.S. AI legislative efforts, studies AI legislative mentions, and explores how lawmakers across the globe perceive and discuss AI. Next, the chapter profiles national AI strategies and regulatory efforts in the United States and the European Union. Finally, it concludes with a study of public investment in AI within the United States.

1. The number of AI regulations in the United States sharply increases.
2. The United States and the European Union advance landmark AI policy action.
3. AI captures U.S. policymaker attention.
4. Policymakers across the globe cannot stop talking about AI.
5. More regulatory agencies turn their attention toward AI.

Chapter 8: Diversity

The demographics of AI developers often differ from those of users. For instance, a considerable number of prominent AI companies and the datasets utilized for model training originate from Western nations, thereby reflecting Western perspectives. The lack of diversity can perpetuate or even exacerbate societal inequalities and biases. This chapter delves into diversity trends in AI. The chapter begins by drawing on data from the Computing Research Association (CRA) to provide insights into the state of diversity in American and Canadian computer science (CS) departments. A notable addition to this year’s analysis is data sourced from Informatics Europe, which sheds light on diversity trends within European CS education. Next, the chapter examines participation rates at the Women in Machine Learning (WiML) workshop held annually at NeurIPS. Finally, the chapter analyzes data from Code.org, offering insights into the current state of diversity in secondary CS education across the United States. The AI Index is dedicated to enhancing the coverage of data shared in this chapter. Demographic data regarding AI trends, particularly in areas such as sexual orientation, remains scarce. The AI Index urges other stakeholders in the AI domain to intensify their endeavors to track diversity trends associated with AI and hopes to comprehensively cover such trends in future reports.

1. U.S. and Canadian bachelor’s, master’s, and PhD CS students continue to grow more ethnically diverse.
2. Substantial gender gaps persist in European informatics, CS, CE, and IT graduates at all educational levels.
3. U.S. K–12 CS education is growing more diverse, reflecting changes in both gender and ethnic representation.

Chapter 9: Public Opinion

As AI becomes increasingly ubiquitous, it is important to understand how public perceptions regarding the technology evolve. Understanding this public opinion is vital in better anticipating AI’s societal impacts and how the integration of the technology may differ across countries and demographic groups. This chapter examines public opinion on AI through global, national, demographic, and ethnic perspectives. It draws upon several data sources: longitudinal survey data from Ipsos profiling global AI attitudes over time, survey data from the University of Toronto exploring public perception of ChatGPT, and data from Pew examining American attitudes regarding AI. The chapter concludes by analyzing mentions of significant AI models on Twitter, using data from Quid.

1. People across the globe are more cognizant of AI’s potential impact—and more nervous.
2. AI sentiment in Western nations continues to be low, but is slowly improving.
3. The public is pessimistic about AI’s economic impact.
4. Demographic differences emerge regarding AI optimism.
5. ChatGPT is widely known and widely used.

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AI Institute and Author Rankings by Publications

Core Areas: AIRankings includes the following six core areas of AI: Computer Vision, Natural Language, Machine Learning, Cognitive Reasoning, Robotics, and Multi-Agent Systems. In addition, we also take into account two areas: Simulation for training AI agents in graphics environments like VR and AR; and AI In General that encompasses a broad spectrum of fields in AI, which is not equivalent to the concept of artificial general intelligence. These eight areas play in synergy and have been integrated towards building general AI systems.
Author Scores: Each author has two scores. Adjusted Publications is the author's total publications in selected areas, adjusted by two factors: an article is weighted by the importance of its venue, and an article with K senior co-authors (not counting students) will give 1/K score to each senior co-author. AI Index is the geometric average of adjusted publications for each selected area. It measures the breadth of an author, and gives a higher score to one with publications across multiple AI areas than another with the same number of publications focusing on a single area.
Institute Scores: An institute's Adjusted Publications and AI Index are similar to those of an author, but including the publications of all its senior authors. When an author changes affiliations, his/her historical scores move with him/herself to the new institute. This design is based on the assumption that the level of research of an institute is determined by its current talents, rather than historical achievements.

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Related research and data

Artificial intelligence has advanced despite having few resources dedicated to its development – now investments have increased substantially
Affiliation of research teams building notable AI systems, by year of publication
Annual attendance at major artificial intelligence conferences
Annual global corporate investment in artificial intelligence, by type
Annual granted patents related to artificial intelligence, by industry
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Annual patent applications related to AI per million people
Annual patent applications related to AI, by status
Annual patent applications related to artificial intelligence
Annual private investment in artificial intelligence CSET
Annual private investment in artificial intelligence NetBase Quid
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Countries with national artificial intelligence strategies
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ImageNet: Top-performing AI systems in labeling images
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Market share for logic chip production, by manufacturing stage
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Parameters vs. training dataset size in notable AI systems, by researcher affiliation
Protein folding prediction accuracy
Scholarly publications on artificial intelligence per million people
Share of artificial intelligence jobs among all job postings
Share of companies using artificial intelligence technology
Share of computer science PhDs specializing in artificial intelligence in the US and Canada
Share of notable AI systems by domain
Share of notable AI systems by researcher affiliation
Share of women among new artificial intelligence and computer science PhDs, United States and Canada
Test scores of AI systems on various capabilities relative to human performance
Top performing AI systems in coding, math, and language-based knowledge tests
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AI Index Report

Read the 2024 AI Index Report

The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year’s edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

Steering Committee Co-Directors

Ray Perrault

Steering committee members.

Erik Brynjolfsson

John Etchemendy

Katrina Ligett

Terah Lyons

James Manyika

Juan Carlos Niebles

Vanessa Parli

Yoav Shoham

Russell Wald

Staff members.

Loredana Fattorini

Nestor Maslej

Letter from the co-directors.

A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.

Progress accelerated in 2023. New state-of-the-art systems like GPT-4, Gemini, and Claude 3 are impressively multimodal: They can generate fluent text in dozens of languages, process audio, and even explain memes. As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.

AI faces two interrelated futures. First, technology continues to improve and is increasingly used, having major consequences for productivity and employment. It can be put to both good and bad uses. In the second future, the adoption of AI is constrained by the limitations of the technology. Regardless of which future unfolds, governments are increasingly concerned. They are stepping in to encourage the upside, such as funding university R&D and incentivizing private investment. Governments are also aiming to manage the potential downsides, such as impacts on employment, privacy concerns, misinformation, and intellectual property rights.

As AI rapidly evolves, the AI Index aims to help the AI community, policymakers, business leaders, journalists, and the general public navigate this complex landscape. It provides ongoing, objective snapshots tracking several key areas: technical progress in AI capabilities, the community and investments driving AI development and deployment, public opinion on current and potential future impacts, and policy measures taken to stimulate AI innovation while managing its risks and challenges. By comprehensively monitoring the AI ecosystem, the Index serves as an important resource for understanding this transformative technological force.

On the technical front, this year’s AI Index reports that the number of new large language models released worldwide in 2023 doubled over the previous year. Two-thirds were open-source, but the highest-performing models came from industry players with closed systems. Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark; performance on the benchmark has improved by 15 percentage points since last year. Additionally, GPT-4 achieved an impressive 0.97 mean win rate score on the comprehensive Holistic Evaluation of Language Models (HELM) benchmark, which includes MMLU among other evaluations.

Although global private investment in AI decreased for the second consecutive year, investment in generative AI skyrocketed. More Fortune 500 earnings calls mentioned AI than ever before, and new studies show that AI tangibly boosts worker productivity. On the policymaking front, global mentions of AI in legislative proceedings have never been higher. U.S. regulators passed more AI-related regulations in 2023 than ever before. Still, many expressed concerns about AI’s ability to generate deepfakes and impact elections. The public became more aware of AI, and studies suggest that they responded with nervousness.

Ray Perrault Co-director, AI Index

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Is China Emerging as the Global Leader in AI?

Daitian Li,
Tony W. Tong,
Yangao Xiao

It has rapidly caught up with the U.S. — but there is no guarantee it’ll pull ahead.

China is quickly closing the once formidable lead the U.S. maintained on AI research. Chinese researchers now publish more papers on AI and secure more patents than U.S. researchers do. The country seems poised to become a leader in AI-empowered businesses, such as speech and image recognition applications. But while China has caught up with impressive speed, the conditions that have allowed it to do so — the open science nature of AI and the nature of the Chinese market, for instance — will likely also prevent it from taking a meaningful lead and leaving the U.S. in the dust.

Twenty years ago, there was a huge gulf between China and the United States on AI research. While the U.S. was witnessing sustained growth in research efforts by both public institutions and private sectors, China was still conducting low-value-added activities in global manufacturing. But in the intervening years, China has surged to rapidly catch up. From a research perspective, China has become a world leader in AI publications and patents. This trend suggests that China is also poised to become a leader in AI-empowered businesses, such as speech and image recognition applications.

DL Daitian Li is an Assistant Professor at the University of Electronic Science & Technology of China. He is an affiliated researcher with the China Institute for Science & Technology Policy at Tsinghua University. He holds a Ph.D. in Business Administration & Management from Bocconi University. His research interests focus on technological catch-up, industry evolution, and technology & innovation management. His research has been published in journals including Research Policy and Tsinghua Business Review .
TT Tony W. Tong is a Professor of Strategy & Entrepreneurship and currently the Senior Associate Dean for Faculty and Research in the Leeds School of Business at the University of Colorado. He studies firm strategy, innovation management, and international business. He has published numerous top journal papers in these areas as well as multiple bestseller case studies in Harvard Business Publishing .
YX Yangao Xiao is a Professor of Management at the University of Electronic Science & Technology of China. His research interests focus on intellectual property rights and latecomer strategies. His research has been published in journals including Research Policy and CEIBS Business Review .

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Artificial intelligence (AI) national strategies provide countries with a framework for the development and implementation of AI technologies. Sixty countries worldwide published their AI national strategies. The majority of these countries with more than 70% are developed countries. The approach of AI national strategies differentiates between developed and developing countries in several aspects including scientific research, education, talent development, and ethics. This paper examined AI readiness assessment in a developing country (Palestine) to help develop and identify the main pillars of the AI national strategy. AI readiness assessment was applied across education, entrepreneurship, government, and research and development sectors in Palestine (case of a developing country). In addition, it examined the legal framework and whether it is coping with trending technologies. The results revealed that Palestinians have low awareness of AI. Moreover, AI is barely used across several sectors and the legal framework is not coping with trending technologies. The results helped develop and identify the following five main pillars that Palestine’s AI national strategy should focus on: AI for Government, AI for Development, AI for Capacity Building in the private, public and technical and governmental sectors, AI and Legal Framework, and international Activities.

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1 Introduction

Artificial intelligence (AI) is a cutting-edge technology (Chatterjee et al. 2022 ). Its applications can be found in many fields including computer science, banking, agriculture, and healthcare (Pham et al. 2020 ; Zhang et al. 2020 ). AI has two domains: Weak AI and Strong AI. Weak AI is specialized for specific tasks, while Strong AI aims to create machines with human-like general intelligence. Developed countries lead in these advancements with advanced technologies and ample resources (Tizhoosh and Pantanowitz 2018 ).

Nations have recognized the transformational potential of AI (Fatima et al. 2020 ). Therefore, more than 60 countries published their AI national strategies in the past 5 years following Canada which was the first to publish the strategy in 2017 (Vats et al. 2022 ; Zhang et al. 2021 ). The majority of countries (more than 70%) who launched their AI national strategies are developed countries (Holon IQ 2020 ).

The approach of AI national strategies differentiates between developed and developing countries. Developed countries have advanced economies and strong technological infrastructures, focusing on leveraging AI to maintain their competitive advantage and drive economic growth. They are at the forefront of both Weak and Strong AI. In the domain of Weak AI, they utilize specialized systems for practical applications in various industries such as healthcare, finance, and transportation, resulting in efficiency gains and economic advantages. In Strong AI endeavors, their significant research involves infrastructure, financial resources, and access to top talent propel innovation. These countries prioritize the development of ethical AI frameworks, invest in education and workforce development, and foster global collaborations to sustain their AI leadership. Their dedication to AI innovation places them at the vanguard of technology, shaping the future of AI-driven industries and applications.

Developing countries, on the other hand, are navigating the AI landscape with varying degrees of progress in Weak and Strong AI. In Weak AI applications, they often rely on cost-effective solutions, such as chatbots or data analytics, to address local challenges like healthcare access and agriculture optimization. However, resource limitations hinder their full adoption. The pursuit of Strong AI remains a challenge due to inadequate research infrastructure and funding constraints. Developing nations prioritize capacity building, fostering local talent, and seeking international collaborations to bridge the AI technology gap. While progress is gradual, their commitment to AI development is a crucial step toward unlocking future socio-economic benefits. The difference between developed and developing countries is expected since developing countries are consumers of technologies produced by developed countries (Monasterio Astobiza et al. 2022 ). Moreover, developing countries have low awareness of applications of AI across several fields (Kahn et al. 2018 ). This increases the gap of AI technology development between developed and developing countries (Kahn et al. 2018 ).

Several developed and developing countries in the MENA region are coping with AI and developing their AI national strategies. According to Google report, the potential economic impact of AI on the Middle East and North Africa (MENA) region is estimated to 320 billion USD dollars by 2030 (Economist Impact 2022 ). Currently, the following 7 countries out of 19 in MENA region launched their AI national strategies: United Arab Emirates, Qatar, Saudi Arabia, Egypt, Oman, Tunisia, and Jordan.

Following other countries in the MENA region, in 2021, the Ministry of Telecom and Information Technology in Palestine issued the need for an AI national strategy. Palestine has good infrastructure since 92% of Palestinian households have home Internet access and in 2022, the optical fiber network was established in Palestine. Therefore, this paper aims to identify AI national strategy pillars in Palestine which is a case of a developing country in the MENA region. To achieve this, the paper assessed the AI status across education, entrepreneurship, government, and research and development sectors in Palestine (the case of a developing country). In addition, it examined the legal framework and whether it is coping with trending technologies.

The paper is structured as follows. Section 2 provides a brief review of AI national strategies in developed and developing countries. The AI readiness assessment in Palestine as a case of a developing country is explained in Sect. 3 . Section 4 presents the results obtained which are essential to identify the Palestinian AI national strategy’s main pillars. Finally, conclusions and future work are depicted in Sect. 5 .

2 Literature review

In recent years, many countries have developed national strategies for AI, which provide a framework for the development and implementation of the technology. These strategies have focused on different pillars, depending on the specific country and its needs (Jorge et al. 2022 ; Economist Impact 2022 ; Kazim et al. 2021 ; Escobar and Sciortino 2022 ).

The use of AI benefits both developed and developing countries (Makridakis 2017 ). However, the ways in which these countries approach AI can be quite different. In general, developed countries have the resources and infrastructure necessary to support the development and implementation of advanced AI technologies (Mhlanga 2021 ). As a result, AI national strategies in these countries focus on using technology to improve efficiencies and productivity in various industries, such as healthcare, finance, and transportation (Ahmed et al. 2022 ; Wahl et al. 2018 ; Kshetri 2021 ; Abduljabbar et al. 2019 ). In contrast, developing countries have more limited resources and infrastructure, so their AI national strategies tend to focus on using technology to address specific needs in their communities. For example, a developing country prioritizes using AI to improve access to education or healthcare or to promote economic growth (Ahmed et al. 2022 ; Guo and Li 2018 ). Additionally, developing countries are focused on using AI to help bridge the gap between themselves and developed countries, in terms of technological advancement and economic growth (Goralski and Tan 2020 ). Overall, the AI national strategies of developed and developing countries tend to differ in terms of their focus and priorities.

This section provides a comprehensive review of existing AI national strategies, and how they differentiate in developed and developing countries.

2.1 AI national strategies in developed countries

Many developed countries have recognized the potential of AI to drive economic growth, improve public services, and advance scientific research. As a result, they have developed national strategies to support the development and deployment of AI technologies in a way that is responsible, ethical, and beneficial to society (Zhang et al. 2021 ).

The United States, released a national AI strategy called the “American AI Initiative” in 2019, which focused on promoting public–private partnerships, investing in AI research and development, and increasing access to data and computing resources for AI researchers (Johnson 2019 ). The initiative is based on the following five key pillars:

Investing in research and development: The United States is investing in AI-focused research institutions and incubators, and is providing support for businesses that are developing AI-related products and services.

Fostering public–private partnerships: The United States is promoting collaboration between government agencies, academia, and the private sector to advance AI research and development.

Promoting the responsible and ethical use of AI: The United States is implementing policies and initiatives to promote the responsible and ethical use of AI by engaging with stakeholders and addressing potential negative impacts of AI.

Supporting the growth of the AI industry: The United States is providing support for businesses that are developing AI-related products and services, and is implementing policies to support the growth of the AI industry.

Building the technological infrastructure and capabilities needed to enable the use of AI: The United States is investing in the development of the technological infrastructure and capabilities needed to enable the use of AI, by implementing policies to support the growth of the AI industry.

Canada has implemented the Pan-Canadian Artificial Intelligence Strategy, which is focused on supporting the growth of the AI industry, and on using AI to address challenges in areas such as healthcare and transportation (Escobar and Sciortino 2022 ). The strategy is based on the following four key pillars:

Investing in research and development: Canada is investing in AI-focused research institutions and incubators, and is providing support for businesses that are developing AI-related products and services.

Supporting the growth of the AI industry: Canada is providing support for businesses that are developing AI-related products and services, and is implementing policies to support the growth of the AI industry.

Using AI to address challenges: Canada is using AI to address challenges in areas such as healthcare and transportation, by implementing AI-powered solutions and initiatives.

Building the technological infrastructure and capabilities needed to enable the use of AI: Canada is investing in the development of the technological infrastructure and capabilities needed to enable the use of AI, by implementing policies to support the growth of the AI industry.

The United Kingdom also launched its strategy “AI Sector Deal” in 2018. This strategy includes a number of initiatives to support the growth of the country’s AI industry, including investments in AI research and development, the establishment of an AI skills institute, and the creation of an AI advisory council to help develop ethical guidelines for the use of AI (Bourne 2019 ). The strategy is based on key pillars similar to the USA.

In Europe, the European Union has also been working on a comprehensive AI strategy “EU AI Strategy”, which includes initiatives to support the development and deployment of AI technologies, as well as measures to ensure the responsible and ethical use of AI (European Commission 2020 ; Cohen et al. 2020 ). The EU AI Strategy is based on three key pillars:

Investing in research and development: The European Union is investing in AI-focused research institutions and incubators, and is providing support for businesses that are developing AI-related products and services.

Supporting the growth of the AI industry: The European Union is providing support for businesses that are developing AI-related products and services, and is implementing policies to support the growth of the AI industry.

Addressing ethical and societal concerns related to AI: The European Union is implementing policies and initiatives to address ethical and societal concerns related to AI, by engaging with stakeholders and promoting the responsible and ethical use of AI.

Other developed countries, such as Japan and South Korea, are also taking steps to develop national AI strategies. Japan has developed the Society 5.0 initiative, which aims to use AI and other emerging technologies to drive economic growth and social development (Fukuyama 2018 ; Shiroishi et al. 2018 ). The Society 5.0 initiative is based on four key pillars similar to Canada.

South Korea has adopted the AI National Development Plan, which is focused on investing in AI research and development, supporting the growth of the AI industry, and promoting the use of AI in various sectors (Chung 2020 ). The AI National Development Plan is based on three key pillars:

Investing in research and development: South Korea is investing in AI-focused research institutions and incubators, and is providing support for businesses that are developing AI-related products and services.

Supporting the growth of the AI industry: South Korea is providing support for businesses that are developing AI-related products and services, and is implementing policies to support the growth of the AI industry.

Promoting the use of AI in various sectors: South Korea is promoting the use of AI in various sectors, by implementing AI-powered solutions and initiatives in areas such as healthcare and transportation.

AI is also increasingly adopted by a number of developing countries in MENA region, including the United Arab Emirates (UAE), Saudi Arabia, and Qatar (Radu 2021 ; Malkawi 2022 ; Ghazwani et al. 2022 ; Alelyani et al. 2021 ). These countries have made significant investments in the development and use of AI technologies, and have implemented a number of initiatives and policies to support the growth of the AI industry (Sharfi 2021 ). For example, the UAE has established partnerships with leading tech companies to develop AI-powered healthcare solutions, and has launched initiatives to support the use of AI in education (Dumas et al. 2022 ; Bhattacharya and Nakhare 2019 ). Saudi Arabia has also invested heavily in research and development in AI, and has implemented policies to support the growth of the AI industry (Bugami 2022 ).

2.2 AI national strategies in developing countries

Many developing countries are still in the early stages of developing and implementing AI national strategies, as the technology is relatively new and can be expensive to implement (Radu 2021 ; Sharma et al. 2022 ). In addition, developing countries face challenges such as limited access to technology and funding, as well as a shortage of skilled workers with expertise in AI (De-Arteaga et al. 2018 ; Sharma et al. 2022 ). As a result, it is likely that the adoption of AI in developing countries will be slower compared to more developed countries.

Regardless of the limited resources and slow adoption of AI, several developing countries have launched AI national strategies following developed countries for several reasons. First and foremost, AI has the potential to benefit developing countries, by providing innovative solutions to challenges and needs in these countries (Strusani and Houngbonon 2019 ). For example, AI-powered healthcare systems can help to improve the availability of medical services in underserved communities (Ilhan et al. 2021 ).

Additionally, developing countries aim to participate in the global AI ecosystem. As AI becomes more prevalent, there is an increasing demand for skilled AI professionals, and developing countries can play a significant role in meeting this demand (Su et al. 2021 ; Squicciarini and Nachtigall 2021 ; Millington 2017 ). By investing in AI education and training, developing countries can help to develop a skilled workforce that is capable of participating in the global AI industry (Millington 2017 ; Sharma et al. 2022 ).

India, Brazil, Mexico, and South Africa developed their AI national strategies which are focused on using AI to address challenges in areas such as healthcare, agriculture, and education, and on building the technological infrastructure and capabilities needed to enable the use of AI (Chatterjee 2020 ; Malerbi and Melo 2022 ; Criado et al. 2021 ; Arakpogun et al. 2021 ). China has implemented the “Next Generation Artificial Intelligence Development” Plan, which is focused on investing in AI research and development, supporting the growth of the AI industry, and promoting the use of AI in various sectors.

Developing countries in the MENA region also launched their AI national strategies or recognized the importance of AI and are currently in the process. Three out of thirteen developing countries in the MENA region (Egypt, Tunisia, and Jordan) launched their AI national strategies (Ministry of Communications and Innovation Technology (Egypt) 2021 ). Their strategies focused on the following pillars:

Building human capacities, expertise, and spreading awareness on AI (develop the capabilities of senior government and private sector leaders in the field of AI).

Importance of participating in AI international and regional conferences and seminars.

Promoting the use and adoption of artificial intelligence and its applications in the public sector and building the necessary partnerships with the private sector

Integrating AI in entrepreneurship and business.

Upskilling employees working in the technology field.

Conducting training for government agencies.

Develop policies related to ethical guidelines, legislative reforms, and standardization.

Develop AI educational courses that could be taught at schools and universities.

As mentioned earlier developing countries recognize the potential benefits of AI, and are taking steps to incorporate it into their economies and societies. This is similar to the current situation in Palestine, as in 2021, the Ministry of Telecom and Information Technology issued the need for an AI national strategy. To develop the strategy, the AI readiness assessment is needed to examine the status of AI in the educational sector (schools, universities), entrepreneurship sector, research and development, governmental sector, and privacy and data Protection. In Palestine, no data is available. Therefore, Sect. 3 illustrates the research methodology which explains in detail the experiment setup needed to identify the main pillars of the Palestinian AI national strategy.

3 Methodology

This paper aims to present AI readiness assessment in Palestine to help develop and identify the main pillars in the AI national strategy. This section describes the experiment questions, the experimental setup, and the participants.

3.1 Research questions

This experiment aims to answer the following main questions:

Do Palestinians have awareness of artificial intelligence?

What is the status of AI across education, entrepreneurship, government, research and development, and sectors in Palestine?

What are the main pillars of the Palestinian AI national strategy?

3.2 Experimental setup

To address the research questions mentioned above, the AI readiness assessment was examined across the educational sector (schools, universities), entrepreneurship sector, research and development sector, governmental sector, and privacy and data protection in Palestine. No data are available in Palestine related to this topic. Therefore, the following data collection methodologies were applied:

One-to-one interviews with experts from the private, public, government, and educational sectors inside and outside Palestine were conducted between 1/9/2021 and 30/8/2022. The experts were presented with a set of interview questions that focused on the current status of AI in their domain and the opportunities and challenges of applying AI in Palestine.

Exploratory research to analyze the higher education BSc and MSc programs, and identify AI courses across universities in Palestine. The data were retrieved from the Ministry of Higher Education in Palestine.

Exploratory research to analyze tech-based educational courses at schools in Palestine. The material taught to school students between fifth grade and twelfth grade was analyzed to assess their coverage of AI-related topics.

Focus groups to assess school students’ and teachers’ awareness of artificial intelligence and identify the existing gaps.

Focus group with MSc students enrolled in AI-related topics.

Questionnaire to assess the Palestinian community’s awareness of AI and identify the existing gaps. The questionnaire consisted of 25 questions to assess participants’ knowledge of AI, AI applications, and gaps to apply AI in Palestine. The questionnaire focused on awareness of Weak AI.

3.3 Participants

Three different groups of participants were involved in the study and informed consent was obtained. The first group included 45 key experts (45+ interview hours) from the private, public, government, and educational sectors inside and outside Palestine. Experts included ministers, chief executive officers from private companies, banks, non-governmental organizations (NGO), incubators, and accelerators in Palestine.

The second group included the following three focus groups:

Ten MSc students enrolled in AI-related programs.

Eight school teachers teaching technology course.

Forty school students (42.8% females and 57.2% males).

The third group consisted of a sample of 240 (44% males and 55.2% females) participants which represent the Palestinian community as it included representatives from the educational, governmental, and private sectors.

4 Results and discussion

This section illustrates the research questions and presents the results obtained.

4.1 Awareness of Palestinians about artificial intelligence

What is your assessment of the level of awareness of the following aspects of AI in Palestine

To assess the level of AI awareness among Palestinians, interviews were conducted with 45 experts and 3 focus groups, and a questionnaire was distributed to a sample of 240 people. The results revealed that Palestinians have low awareness of the concept and applications of AI in public, private, educational, leadership, innovation, and research and development sectors. The results of the questionnaire also confirmed that the following topics were not discussed in the field of AI in Palestine (Fig. 1 ):

Opportunities and risks of AI in the government digital transformation.

AI opportunities and risks in addressing climate change, water management, and natural disaster risk reduction.

The opportunities and risks of AI in teaching and learning.

Opportunities and risks of AI in creating jobs and contributing to economic growth.

The opportunities and risks of AI on creativity, language, media, and journalism.

Implications for human rights, such as privacy, discrimination, and equality.

4.2 AI in education

This section aims to examine the integration of AI into the Palestinian educational curriculum at schools and universities.

4.2.1 AI in Palestinian schools

Palestinian schools introduced a technology course that is taught to students from the fifth grade to the twelfth grade. The topics related to AI in each grade are summarized in Table 1 .

To assess the knowledge of school students about AI concept and teachers’ perspective on the importance of adding educational materials focusing on AI topics to the Palestinian curriculum, the following two focus groups were carried out:

A focus group with 40 school students (57.2% of participants were male and 42.8% were female) enrolled in grades 5 up to 12 (Fig. 2 )

Eight teachers teaching the technology course at schools.

Percentage of school students participating in focus groups

The results revealed that 42% of students stated that they know the definition of AI (Fig. 3 ). This is expected since the definition is introduced in the educational curriculum. However, there is a gap in students’ understanding of the practical applications of AI. More than 50% of school students did not recognize the practical applications of AI. Figure 4 shows that only 15% of school students knew that AI is used in social medial applications such as TikTok (De Leyn et al. 2021 ). This indicates that students’ have low awareness of the applications of AI.

On the other hand, 90% of the students participating in the study expressed interest to learn more about AI and its applications. Teachers had a similar opinion as they strongly agreed that adding AI-related topics to the Palestinian curriculum is necessary since minimal information is provided in the current curriculum.

School students’ knowledge of the definition of AI

School students’ knowledge of AI applications

4.2.2 AI in Palestinian universities

The AI Index 2021 annual report released by Stanford University revealed that there is a total of 1032 AI programs in 27 European Union countries (Zhang et al. 2021 ). The vast majority of academic programs specialized in AI in the European Union are taught at the master’s level. The programs aim to provide students with strong competencies for the workforce. Germany provides the highest number of programs specialized in AI, followed by the Netherlands, France, and Sweden.

In Palestine, the number of universities and colleges is 55 (Palestinian Ministry of Higher Education and Scientific Research 2022 ), and only 9% of Palestinian universities and colleges offer academic programs specialized in AI. Palestine offers six programs specialized in AI, which is close to other countries in the European Union. These programs constitute only 2.6% of the 224 technological academic programs offered at universities and colleges. The vast majority of these programs (83.3%) are master’s programs and there is still no Ph.D. program specialized in AI.

The results also revealed that the number of graduates from Palestinian colleges and universities specializing in AI between 2016 and 2021 is very low. Table 2 shows that only 28 out of 13,939 students are specialized in AI. Moreover, 60.7% of students are males and 39.3% are females. This indicates the low participation of females in the field of AI, in contrast to their close participation in various technological sectors (Fig. 5 ).

Percentage of male and female graduates in technological disciplines

In 2022, the number of graduates specializing in AI in Palestine doubled by nearly 2.7 (the number of students increased from 28 to 76). However, the number of students enrolled in Palestinian universities specializing in AI constitutes only 0.1% of the 104,499 enrolled in Palestinian universities and colleges from 2016 to 2021, which is a very small percentage. This contributes to the asymmetry between AI skills and industry needs which is currently a pain point in many countries that published their AI national strategies (Vats et al. 2022 ).

The results revealed that Palestine is at a very early stage in terms of the availability of educational resources and trainers. This was confirmed in the interviews with 45 experts and the results of a survey that were published to 240 participants to assess the awareness of the Palestinian community in AI and to identify gaps. The results showed that 53.3% of the sample confirmed that AI educational resources are not available, and 46% of the sample confirmed the lack of expertise in the field of AI (Table 3 ).

Further analysis was carried out with a focus group of ten master’s students enrolled in AI programs at Palestinian universities. The group confirmed the low awareness of the importance of AI in the educational and technological sectors in Palestine. This is due to the lack of AI-applied courses in Palestinian universities. The group also agreed that the labor market in Palestine has become more interested in the field of AI.

4.3 AI in entrepreneurial ecosystem

Palestine has 102 technology-based startups and 94 registered organizations that have worked during the year 2021 and have at least 1 program or project focused on empowering startups (Polaris 2021 ). The vast majority of startups are e-commerce companies, followed by the education and health sector (see Fig. 6 ).

Startups per sector in Palestine (Polaris 2021 )

Further analyses were carried out to examine the usage of AI technology in existing startups. The results revealed that a small percentage of startups use AI (0.09%). This was confirmed in interviews with experts leading technology incubators and accelerators, as they emphasized that the number of AI startups is small and there is not enough expertise to evaluate or supervise startups during the incubation and acceleration process in Palestine.

4.4 AI in research and development

According to EduRank, there are 1.51 million academic publications in the field of AI submitted by 2797 universities in the world (EduRank 2022 ). Table 4 shows the top universities in the world ranked based on their research performance in AI. In the MENA region, the number of publications per university highly decreased and the region achieves merely 5.5% of peer-reviewed AI publications (Economist Impact 2022 ). Table 5 shows the universities in the MENA region with the highest number of publications.

In Palestine, the total publications across universities are less than the publications in the American University of Beirut (see Table 6 ).

The results revealed that Palestine had minimal knowledge in the field of AI. This was also confirmed in the interviews with experts and the results of the questionnaire published to 240 people. Figure 7 shows that 56.5% of the sample confirmed that there is a limited number of research centers and a lack of human resources and expertise in the field of AI. This was also confirmed by the interviews with experts who emphasized that there are no links between global AI expertise and national and global AI researchers.

Status of research and development in Palestine

4.5 AI in governmental sector

The results of the interviews and the questionnaire published to 240 participants showed that 31% of the sample believed that the level of governmental participation in topics related to AI is at an early development stage (see Fig. 8 ).

Level of government participation in topics related to AI

4.6 AI and privacy and protection

Based on the United Nations Conference on Trade and Development (UNCTAD), 128 out of 194 countries had put in place legislation to secure the protection of data and privacy (UNCTAD 2021 ). Table 7 shows the status of protection of data and privacy laws in the MENA region. The status of Privacy and Protection laws in Palestine is also at a very early stage. An exploratory study had been carried out by “7amleh” to study the status of privacy and digital data protection in Palestine (7amleh 2021 ). The results revealed that there are no laws and legislation in Palestine which keep pace with trending technologies. This causes privacy and data protection violations.

This was also confirmed by the research results, as the questionnaire, focus groups, and interviews with experts confirmed that there is a gap in the development of a legal framework that keeps pace with AI. 83.3% of 240 participants confirmed that the legal frameworks have not yet been developed to keep pace with AI in Palestine.

4.7 AI national strategy overview

This section translates the aforementioned findings into a strategic framework that tries to address weaknesses and minimize threats while building on strengths and opportunities. The government sector in Palestine is currently undergoing a significant digital transformation, which inevitably needs to happen concurrently with the implementation of the AI strategy. Additionally, to demonstrate the value of AI across various domains, it is critical to focus on areas where the greatest gains can be made in the short term given that the country has relatively few resources. Therefore, the following sections present the AI national strategy vision and mission statements that spell out precisely what Palestine hopes to accomplish by implementing AI, and where tradeoffs will be made. In addition, it illustrates the objectives and main pillars required to achieve the objectives.

4.7.1 Vision

The AI national strategy vision is “A globally distinguished position in Artificial Intelligence, with sustainable productivity, economic gains, and creation of new areas of growth.”

4.7.2 Mission

The AI national strategy mission is to “Establish an Artificial Intelligence industry in Palestine that includes the development of skills, technology, and infrastructure to ensure its sustainability and competitiveness.”

4.7.3 Goals

To achieve the aforementioned vision and mission, Palestine will work on the following goals:

Support lifelong learning and reskilling programs to contribute to workforce development and sustainability.

Facilitate multi-stakeholder dialogue on the deployment of responsible AI for the benefit of society and encourage relevant policy discussions.

Encourage investment in AI research and entrepreneurship through partnerships between the public and private sectors, initiatives, universities, and research centers.

Make Palestine a regional center and a talent pool in the field of AI by meeting the needs of local and regional markets and attracting international experts and researchers specialized in AI.

Integrating AI technologies into government services to ensure the services are more efficient and transparent.

Use AI in the main development sectors to achieve an economic impact and find solutions to local problems in line with the goals of sustainable development.

Create a thriving environment for AI by encouraging and supporting companies, startups, and scientific research.

Promote a human-centered approach in which people’s well-being is a priority and facilitate multi-stakeholder dialogue on the deployment of responsible AI for the benefit of society.

Using AI as an opportunity to include marginalized people in initiatives that promote human advancement and self-development.

Facilitate cooperation at the local, regional, and international levels in the field of AI.

Contribute to global efforts and international forums on AI ethics, the future of work, responsible AI, and the social and economic impact of AI.

Support the research bridges between Palestinian and international universities in the field of AI.

In addition to the aforementioned goals, the AI national strategy will help achieve the following numeric goals in the upcoming 5 years: having 300 graduates specialized in AI, 100 specialists in the field of AI in Palestine, systematic integration of AI into 4 educational sectors, 30% of the technology startups in Palestine use AI technology, 10% of private companies in Palestine adopt AI-based solutions, 200 published research papers in the field of AI in Palestine, 20 people specializing in privacy and digital data protection, and 50 datasets uploaded into opendata website in Palestine.

4.7.4 AI national strategy pillars

To achieve the goals above, the strategy has been divided into the following five main pillars:

AI for Government: the rapid adoption of AI technology via the automation of governmental procedures and the integration of AI into the decision-making process to improve productivity and transparency.

AI for Development: apply AI to several industries using a staged strategy to realize efficiencies, achieve more economic growth, and improve competitiveness. This could be achieved through domestic and international partnerships.

AI for Capacity Building: spread awareness and provide personalized training to private, public, and governmental sectors.

AI and Legal Framework: develop a legal framework to empower using AI across several sectors.

International Activities: play a key role in fostering cooperation on the regional and international levels by championing relevant initiatives, and actively participating in AI-related discussions and international projects.

These five pillars form a comprehensive approach to the AI national strategies, covering the government’s role, industry-specific implementation, workforce development, legal considerations, and international collaboration. By addressing these dimensions, Palestine can establish a solid foundation for responsible, inclusive, and sustainable AI deployment (Chatterjee 2020 ; Nankervis et al. 2021 ; Barton et al. 2017 ).

4.8 AI national strategy governance

AI national strategy governance is essential to ensure the implementation of AI national strategy. It guides the responsible and effective adoption, development, and use of AI in Palestine. Therefore, in 6/9/2021, the Council of Ministries in Palestine approved the decision to form an AI national team headed by the Ministry of Telecommunications and Information Technology and had 16 representatives from several ministries in Palestine, the private sector, and the educational sector (Telecommunication and Technology 2023 ). The national team is responsible for implementing and managing the AI national strategy in coordination with relevant experts and agencies. Their responsibilities can be summarized as follows:

Establishing a follow-up mechanism for the implementation of the AI national strategy which is consistent with international best practices in this field.

Setting national priorities in the field of AI applications.

Reviewing any form of cooperation at the regional and international levels, including the exchange of best practices and experiences.

Providing recommendations for national policies and plans related to technical, legal, and economic frameworks for AI applications.

Recommending programs for capacity building and to support the AI industry in Palestine.

Reviewing international protocols and agreements in the field of AI.

In addition to the AI national team, an advisory committee has been formed from the private and educational sectors in Palestine to support and assist the AI national team and help them achieve their responsibilities.

5 Conclusion

Sixty countries worldwide published their AI national strategies (Zhang et al. 2021 ). The approach of AI national strategies differentiates between developed and developing countries, since developed countries are consumers of technologies produced by developing countries (Monasterio Astobiza et al. 2022 ). Moreover, developed countries have low awareness of applications of AI across several fields (Kahn et al. 2018 ). This increases the gap in AI technology development between developed and developing countries (Kahn et al. 2018 ).

This paper aims to identify AI national strategy pillars in a developing country. Therefore, the paper assessed the AI status across education, entrepreneurship, government, and research and development sectors in Palestine (the case of a developing country). In addition, it examined the legal framework and whether it is coping with trending technologies.

Three different groups of participants were involved in the study. The first group included 45 experts (45+ interview hours) from the private, public, government, and educational sectors inside and outside Palestine. The second group included three focus groups which consisted of MSc students enrolled in AI-related programs, school teachers, and school students. The third group consisted of a sample of 240 participants which represent the Palestinian community as it included representatives from the educational, governmental, and private sectors.

The results revealed that Palestinians have low awareness of AI. Moreover, AI is barely used across several sectors and the legal framework is not coping with trending technologies. The results helped develop and identify five main pillars Palestine should focus on in the AI national strategy: AI for Government, AI for Development, AI for Capacity Building in the private, public, technical, and governmental sectors, AI and Legal Framework, and International Activities. The pillars will help achieve the following in the upcoming 5 years: having 300 graduates specialized in AI, 100 specialists in the field of AI in Palestine, systematic integration of AI into 4 educational sectors, 30% of the technology startups in Palestine use artificial intelligence techniques, 10% of private companies in Palestine adopt AI-based solutions, 200 published research papers in the field of artificial intelligence in Palestine, 20 people specializing in privacy and digital data protection, and 50 datasets uploaded into opendata website in Palestine. The AI national strategy was approved by the Palestinian cabinet in June 2023.

In the future, further analysis will be carried out to assess Palestinians’ awareness of Weak and Strong AI, in addition to the progress and outcome of AI national strategy across education, entrepreneurship, government, and research and development sectors.

Data availibility

The data analyzed during the current study are available from the corresponding author on reasonable request.

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21 August 2019

Will China lead the world in AI by 2030?

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China not only has the world’s largest population and looks set to become the largest economy — it also wants to lead the world when it comes to artificial intelligence (AI).

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Which countries and universities are leading on AI research?

Elsevier data show that china produces vast quantities of papers in field but lags on quality.

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China has been producing almost twice as many papers on artificial intelligence as the next highest-placed country in terms of publication volume for the field, a data analysis for Times Higher Education has shown.

Data from Elsevier’s Scopus database provided to THE illustrate China’s huge drive on research in the area, with researchers in the nation notching up just over 41,000 publications from 2011 to 2015.

In terms of publication volume, the US was second during the period with almost 25,500 publications while Japan was in third place (about 11,700) and the UK in fourth (about 10,100).

Publications in AI research, 2011 to 2015

However, although China scored high in terms of volume, it was only 34th in terms of field-weighted citation impact (which allows for differences in citations according to subject and year), suggesting that most of the papers were not of the same quality as those coming from the US (fourth for citation impact), for instance.

Source: Elsevier/Scopus

Leading the world on this measure was Switzerland, with a citation impact score of 2.71, followed by Singapore (2.24) and Hong Kong (2.00), although all three of these produced fewer than 2,500 publications on AI over the time frame.

Looking at individual institutions that published more than 500 times on AI shows that only one in China – the Institute of Automation, Chinese Academy of Sciences – had a citation impact above the world average of 1.

The list ranked by citation impact is topped by the Massachusetts Institute of Technology with a score of 3.57. This is way ahead of the rest of the chasing pack, including Carnegie Mellon University and Nanyang Technological University, Singapore .

In recent years, it has been industry – in emerging tech areas such as self-driving cars – that has been the driving force behind the explosion in AI research, both in the West and in China, according to Alexander Wong, Canada research chair and professor in systems design engineering at the University of Waterloo .

Waterloo, the first university in Canada to receive government approval to test a self-driving car on public roads in the country, comes 11th in terms of citation impact for AI research.

“Before the recent revolutions in AI, industry had been very slow in adopting and embracing [the field], but now they…are rapidly adapting their businesses to push the frontiers of AI, and more importantly working closer to researchers and universities to support them in pushing advances in AI at an incredible pace,” Professor Wong said.

Sean Holden, a senior lecturer in computer science at the University of Cambridge , said a key factor in the current growth of AI research was that recent advances “have been in areas that can obviously be monetised” in a manner different from “traditional” uses of AI.

“The assorted boxes that you can talk to in your house – that try to assist you – have only more recently been feasible and represent a completely different way of making revenue. Such avenues are opened when notable advances are made, of which there have recently been several,” he said.

However, he warned that there was a “cycle of hype” surrounding AI which was currently “at its peak”, leading to companies and governments showing more interest. In the long term it was universities that would still have impact in driving the field forward, he said, because they were “more interested in science than generating return for shareholders”.

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Published: 18 April 2024

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

James Shaw 1 , 13 ,
Joseph Ali 2 , 3 ,
Caesar A. Atuire 4 , 5 ,
Phaik Yeong Cheah 6 ,
Armando Guio Español 7 ,
Judy Wawira Gichoya 8 ,
Adrienne Hunt 9 ,
Daudi Jjingo 10 ,
Katherine Littler 9 ,
Daniela Paolotti 11 &
Effy Vayena 12

BMC Medical Ethics volume 25 , Article number: 46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Peer Review reports

Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table 1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table 2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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Infographic: AI Research and Development in the U.S., EU and China

As governments and the general public pay closer attention to artificial intelligence (AI) and options for its regulation, we examine U.S., EU and Chinese progress on AI research and development over the last two decades.

AI Research

The U.S. was a leader in AI research in the early 2000s. Its institutions published more AI-related papers than those in any other country. But in 2006, China took the lead as the source of 58,067 AI publications. The U.S. and the EU trailed with, respectively, 52,671 and 49,540. Chinese researchers have since become even more prolific, publishing 155,487 AI papers in 2022, followed by those in the EU with 101,455 and U.S. researchers’ 81,130. The Chinese accounted for nearly 40% of global AI publications in 2021.

In the 2010s, while China’s technology industry was still developing, U.S. researchers argued that China’s lead in volume of papers did not erase the quality of its research and AI talent. The talent gap continues, as more than half of the best Chinese AI scientists work or pursue graduate degrees outside their country, but the quality of China’s AI research papers has improved significantly over the years. A study from Nikkei Asia measuring the quality of AI research by counting the number of papers in the top 10% of citations in other papers shows that China overtook the U.S. in the quality of its AI research by 2019. By 2021, China accounted for 7,401 of the most-cited papers, 70% more than the number of the most-cited U.S. papers.

American technology companies continue to dominate the AI research space with six corporate giants, including Google, Microsoft and IBM, among the top 10 producers of the most-cited research. Chinese companies are gaining traction, however. Only one company was in the top 10 in 2012; there were four in 2021. Tencent, Alibaba and Huawei have forged ahead in the number of AI papers that they produce and the citations that these publications receive.

Venture Capital Investment in AI

The U.S. continues to lead in attracting the most venture capital investment in AI and data startups, scoring a sharp increase between 2020 and 2021. The bulk of this investment was in mobility and autonomous vehicles (AV), healthcare and biotechnology, and business processes and support services. Investment growth in healthcare and biotechnology is likely a result of the COVID-19 pandemic, as is the growth seen in business services since many workers and students transitioned to hybrid or remote arrangements with the help of platforms like Zoom and Microsoft Teams. Investment in these sectors, however, decreased significantly in 2022 and 2023, and replaced by the $25 billion that is expected to flow into AI-powered marketing on social media platforms in 2023. Financial and insurance services is expected to be the second biggest industry for venture capital investment in 2023 at almost $15 billion.

The EU and China saw a similar trend to that in the U.S. The EU sectors that attracted the most venture capital investment between 2020 and 2022 were business processes and financial and insurance services. The top sector for 2023 is expected to be IT infrastructure, drawing almost $1.5 billion, followed by AI in social media marketing at almost $1 billion, even if both are significantly less than that seen in the U.S. In China, the AV industry has seen a stark increase in investment since 2018. It continues to be the sector attracting the most venture capital there, even if the amount was much lower in 2023 than in 2021. China’s second-biggest sector for such investment in 2021 was robots, sensors and IT hardware, which brought in $10 billion. The figure for 2023 is expected to show a decline to $2 billion.

AI Software Development

The U.S. and EU are ahead of China in AI software development, though not significantly. As the U.S. and EU gradually decrease their contributions to AI software development, the gap with China is closing. This trend is reflected in the software development contributions made to public AI projects by American, European and Chinese developers on GitHub, a platform and cloud-based service for software development and version control. GitHub is the primary platform for developers to store and manage their code and to collaborate.

The Organization for Economic Co-operation and Development (OECD) collects data on the number of GitHub’s AI projects, or AI-related GitHub repositories, and developer contributions made to these projects. Analysis of that data allows identifying AI software developers, their locations, the development tools they use and the level of impact of their AI projects. All this provides insight into the broader trends in software development and innovation. Level of impact is determined by the number of managed copies, or forks, that other developers make of a given AI project. By this measure, U.S. and EU high-impact AI projects declined from 40% and 26% respectively in 2011 to 20% and 16% in 2022. In the same period, the number of high-impact Chinese AI software projects grew from almost 0% to 11.6% in 2022.

Daniela Rojas Medina

Research analyst bertelsmann foundation.

[email protected]

AI Research and Development in the U.S., EU and China

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Stanford just released its annual AI Index report. Here's what it reveals

Figurines with computers and smartphones are seen in front of the words "Artificial Intelligence AI" in this illustration taken, February 19, 2024. REUTERS/Dado Ruvic/Illustration

It's Stanford's 7th AI Index report. Image: REUTERS/Dado Ruvic/Illustration

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Stanford University has released its seventh AI Index report.
It covers trends such as technical advancements in AI and public perceptions of the technology.
In an effort to alleviate concerns around AI governance, the World Economic Forum has spearheaded the AI Governance Alliance .

Artificial intelligence’s (AI) influence on society has never been more pronounced. Since ChatGPT became a ubiquitous feature on computer desktops in late 2022, the rapid development and deployment of generative AI and large language model (LLM) tools have started to transform industries and show the potential to touch many aspects of modern life.

AI has even surpassed human-level performance on several benchmark tasks and is succeeding in helping workers become more productive and produce better-quality work. That’s according to Stanford University’s AI Index report .

The report, which is in its seventh edition, covers trends such as technical advancements in AI, public perceptions of the technology and the geopolitical dynamics surrounding its development.

Here are 10 key takeaways.

1. AI is outperforming humans on various tasks

A graph showing AI technical performance benchmarks versus human performance

As of 2023, AI is surpassing human performance on some benchmarks, including in image classification, visual reasoning and English understanding. However, there are still some task categories where AI fails to exceed human ability, most notably complex cognitive tasks. These tasks include the likes of visual common-sense reasoning and planning, and competition-level mathematics.

2. Industry takes the lead

Until 2014, academia led in the release of machine learning models. That’s no longer the case. In 2023, there were 51 machine learning models produced by industry compared with just 15 from academia. Interestingly, 21 notable models were created in 2023 as a result of industry-academia collaborations, which represents a new high.

What’s behind industry’s phenomenal uplift? Creating cutting-edge AI models now demands a substantial amount of data, computing power and financial resources, which are not typically accessible in academia.

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Ai is helping to identify skills gaps and future jobs. an expert explains how, ai and emerging technology at davos 2024: 5 surprising things to know, eu sets global standards with first major ai regulations: here's what you need to know, confused about ai here are the podcasts you need on artificial intelligence, 3. frontier models reach unprecedented costs.

As mentioned before, LLMs aren’t cheap to run or train. According to AI Index estimates, the training costs of leading AI models have increased significantly. For example, OpenAI’s GPT-4 training costs were estimated to be $78 million, while Gemini Ultra by Google cost $191 million.

In comparison, back in 2017, the original Transformer model , which is recognized as introducing the architecture that underpins virtually all modern LLMs, cost around $900 to train.

4. The United States is the leading source of top AI models

A graph showing the number of notable machine learning models launched by country in 2023

To gain an understanding of the evolving geopolitical landscape of AI development, the AI Index research team analyzed the country of origin of notable models. The results showed that, in 2023, the United States leads, with 61 notable models, outpacing the European Union’s 21 and China’s 15. Since 2003, the US has produced more models than other major geographic regions.

5. Standardized benchmark reporting for responsible AI is lacking

The effectiveness of benchmarks when it comes to AI tools largely depends on their standardized approach and application. However, research from the AI Index reveals a significant lack of standardization in responsible AI reporting. For instance, leading developers, including OpenAI, Google and Anthropic, primarily test their models against different responsible AI benchmarks. These different testing models on different benchmarks complicate comparisons, as individual benchmarks have unique natures. Standardized benchmark testing is considered critical to enhance transparency around AI capabilities.

6. Investment in generative AI is sky-high

While overall AI private investment decreased in 2023, funding for generative AI sharply increased. The sector attracted $25.2 billion last year, nearly nine times the investment of 2022 and about 30 times the amount in 2019. Generative AI accounted for over a quarter of all AI-related private investment in 2023.

In response to the uncertainties surrounding generative AI and the need for robust AI governance frameworks to ensure responsible and beneficial outcomes for all, the Forum’s Centre for the Fourth Industrial Revolution (C4IR) has launched the AI Governance Alliance .

The Alliance will unite industry leaders, governments, academic institutions, and civil society organizations to champion responsible global design and release of transparent and inclusive AI systems.

7. AI is making workers more productive and creating higher quality work

While using AI without proper oversight can lead to diminished performance, several studies assessing AI’s impact on labour suggest that it enables workers to complete tasks more quickly and improved the quality of their output. The studies also showed AI’s potential to bridge the skills gap between low- and high-skilled workers.

8. AI is playing a growing role in scientific progress

While 2022 saw AI begin to advance scientific discovery, 2023 made further leaps in terms of science-related AI application launches, says the AI Index. Examples include Synbot, an AI-driven robotic chemist for synthesizing organic molecules, and GNoME, which discovers stable crystals for the likes of robotics and semiconductor manufacturing.

9. AI regulations in the US are on the rise

A chart showing the number of AI-related regulations introduced in the United States from 2016-2023

In 2023, 25 AI-related regulations were enacted in the US, growing the total number by 56.3%. Compare that to 2016, when just one was introduced.

The number of AI-related regulations passed by the EU jumped from 22 in 2022 to 32 in 2023. Despite this growth, approved EU regulations were at their peak in 2021, when 46 were passed.

10. People are more aware – and nervous of – AI’s impact

The report includes information from an Ipsos survey that shows, over the past year, that the proportion of people who think AI will dramatically affect their lives in the next three to five years has increased from 60% to 66%.

Unease towards AI products and services saw a 13 percentage point rise from 2022, with 55% reported to feel nervous. The report also cites Pew data that states that 52% of Americans feel more concerned than excited about AI, up from 38% in 2022.

In an effort to alleviate concerns about AI governance globally, the World Economic Forum has established a group called the AI Governance Alliance . Consisting of industry leaders, governments, academic institutions and civil society organizations, it aims to promote the creation of transparent and inclusive AI systems globally.

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Title: phi-3 technical report: a highly capable language model locally on your phone.

Abstract: We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide some initial parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on MT-bench).

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THE AI INDEX REPORT

ai iNDEX anNUAL rEPORT

TOP TAKEAWAYS

2. Industry continues to dominate frontier AI research .

3. Frontier models get way more expensive .

5. Robust and standardized evaluations for LLM responsibility are seriously lacking.

6. Generative AI investment skyrockets.

7. The data is in: AI makes workers more productive and leads to higher quality work.

8. Scientific progress accelerates even further, thanks to AI.

9. The number of AI regulations in the United States sharply increases.

10. People across the globe are more cognizant of AI’s potential impact—and more nervous.

Chapter 1: Research and Development

Chapter 2: Technical Performance

Chapter 3: Responsible AI

Chapter 4: Economy

Chapter 5: Science and Medicine

Chapter 6: Education

Chapter 7: Policy and Governance

Chapter 8: Diversity

Chapter 9: Public Opinion

Past Reports

AI Institute and Author Rankings by Publications

Related research and data

AI Index Report

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Towards a new generation of artificial intelligence in China

1 Introduction

2 Literature review

2.1 AI national strategies in developed countries

2.2 AI national strategies in developing countries

3 Methodology

3.1 Research questions

3.2 Experimental setup

3.3 Participants

4 Results and discussion

4.1 Awareness of Palestinians about artificial intelligence

4.2 AI in education

4.2.1 AI in Palestinian schools

4.2.2 AI in Palestinian universities

4.3 AI in entrepreneurial ecosystem

4.4 AI in research and development

4.5 AI in governmental sector

4.6 AI and privacy and protection

4.7 AI national strategy overview

4.7.1 Vision

4.7.2 Mission

4.7.3 Goals

4.7.4 AI national strategy pillars

4.8 AI national strategy governance

5 Conclusion

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