Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·28d ago·5 min read
Whose fairness? Structural concentration in AI bias research
TL;DR
AI bias research often overlooks the structural concentration of power and resources, leading to skewed fairness assessments. By analyzing the distribution of research funding and publication venues, the study reveals significant disparities in representation.
✦ Why It Matters
Today, prioritize collaborations with diverse institutions to enrich AI fairness research and broaden perspectives.
Key Takeaways
- Assess the geographic diversity of your AI research collaborators.
- Incorporate perspectives from low- and middle-income countries in your projects.
- Review the citation influence of your sources to avoid bias.
- Utilize the interactive atlas to stay updated on AI bias research trends.
Full Summary
AI systems are increasingly used in critical areas like healthcare and law, necessitating robust methods for measuring and mitigating bias. However, the definitions and benchmarks for fairness in AI are largely shaped by a small, concentrated group of researchers, predominantly from the United States.
An analysis of 692 publications revealed that a few countries and authors dominate the field, with the U.S. leading in output and collaboration. Low- and middle-income countries are notably underrepresented, and citation influence is skewed, indicating that a small number of publications disproportionately shape the discourse.
This concentration in the foundational domain of general fairness raises concerns that the developed mitigation methods may not be applicable across diverse populations and settings. An interactive atlas has been created to monitor the evolving structure of AI bias research.