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Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence

arXiv 1907.07892 ↗First posted 18 Jul 2019cs.CYcited by 21

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Abstract (arXiv)

The ethical implications and social impacts of artificial intelligence have become topics of compelling interest to industry, researchers in academia, and the public. However, current analyses of AI in a global context are biased toward perspectives held in the U.S., and limited by a lack of research, especially outside the U.S. and Western Europe. This article summarizes the key findings of a literature review of recent social science scholarship on the social impacts of AI and related technologies in five global regions. Our team of social science researchers reviewed more than 800 academic journal articles and monographs in over a dozen languages. Our review of the literature suggests that AI is likely to have markedly different social impacts depending on geographical setting. Likewise, perceptions and understandings of AI are likely to be profoundly shaped by local cultural and social context. Recent research in U.S. settings demonstrates that AI-driven technologies have a pattern of entrenching social divides and exacerbating social inequality, particularly among historically-marginalized groups. Our literature review indicates that this pattern exists on a global scale, and suggests that low- and middle-income countries may be more vulnerable to the negative social impacts of AI and less likely to benefit from the attendant gains. We call for rigorous ethnographic research to better understand the social impacts of AI around the world. Global, on-the-ground research is particularly critical to identify AI systems that may amplify social inequality in order to mitigate potential harms. Deeper understanding of the social impacts of AI in diverse social settings is a necessary precursor to the development, implementation, and monitoring of responsible and beneficial AI technologies, and forms the basis for meaningful regulation of these technologies.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2019.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 3; 2024: 6; 2025: 5.

21 papers.

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  1. 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 102
  2. 2021Will Artificial Intelligence supersede Earth System and Climate Models?cited by 37
  3. 2021Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactionscited by 35
  4. 2023Human-AI Coevolutioncited by 27
  5. 2021Reliability Testing for Natural Language Processing Systemscited by 25
  6. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  7. 2022Towards Responsible Natural Language Annotation for the Varieties of Arabiccited by 14
  8. 2021Understanding and Avoiding AI Failures: A Practical Guidecited by 6
  9. 2024Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulationcited by 5
  10. 2022How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in Indiacited by 4
  11. 2023What We Know So Far: Artificial Intelligence in African Healthcarecited by 2
  12. 2024AI and Identitycited by 2
9 more
  1. 2025Limits to AI Growth: The Ecological and Social Consequences of Scalingcited by 2
  2. 2023Are You Worthy of My Trust?: A Socioethical Perspective on the Impacts of Trustworthy AI Systems on the Environment and Human Societycited by 1
  3. 2024(Unfair) Norms in Fairness Research: A Meta-Analysiscited by 1
  4. 2024"My Kind of Woman": Analysing Gender Stereotypes in AI through The Averageness Theory and EU Lawcited by 1
  5. 2025Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Studycited by 1
  6. 2025Understanding Critical Thinking in Generative Artificial Intelligence Use: Development, Validation, and Correlates of the Critical Thinking in AI Use Scalecited by 1
  7. 2024Evaluating the Capabilities of LLMs for Supporting Anticipatory Impact Assessmentcited by 0
  8. 2025AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patternscited by 0
  9. 2025Bridging the AI Trustworthiness Gap between Functions and Normscited by 0