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

Alexa Hagerty, Igor Rubinov|arXiv (Cornell University)|Jul 18, 2019

Ethics and Social Impacts of AI107 citations

TL;DR

The paper surveys AI ethics and social impacts across five global regions, highlighting regional variation, inequality amplification, and the need for ethnographic, on-the-ground research to guide responsible AI regulation.

ABSTRACT

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.

Motivation & Objective

• Motivate a global, non-U.S.-centric understanding of AI ethics and social impacts.
• Synthesize findings from a large, multilingual literature base to identify regional variations in AI effects.
• Highlight potential amplification of social inequality and vulnerability in low- and middle-income countries.
• Advocate for rigorous ethnographic, on-the-ground research to inform regulation and governance of AI technologies.

Proposed method

• Conduct a literature review of social science scholarship on AI and related technologies across five global regions.
• Screen and synthesize insights from more than 800 academic journal articles and monographs in over a dozen languages.
• Identify patterns in how AI-driven technologies affect social divides and inequality in different contexts.
• Draw out implications for regulation, governance, and responsible AI development based on cross-regional evidence.
• Emphasize the need for ethnographic methods to deepen understanding of local social contexts.

Experimental results

Research questions

• RQ1 How do social impacts and ethical implications of AI vary across different global regions?
• RQ2 In what ways does AI access and deployment reinforce or mitigate social inequalities in diverse cultural contexts?
• RQ3 What kinds of on-the-ground, ethnographic evidence are needed to responsibly regulate AI technologies globally?

Key findings

• AI’s social impacts are likely to differ markedly by geographic and cultural settings.
• Perceptions and understandings of AI are shaped by local contexts, influencing adoption and governance.
• AI-driven technologies may entrench social divides and exacerbate inequality, as seen in U.S. settings and suggested globally.
• Low- and middle-income countries may be more vulnerable to negative AI impacts and less likely to gain benefits.
• There is a global pattern of inequality amplification by AI that requires rigorous, local ethnographic research to understand and mitigate harms.
• Better regulation and responsible AI require deep, contextual knowledge of diverse social settings.