Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations
arXiv:2511.05613v2 Announce Type: replace-cross Abstract: Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general capability evaluations are widespread, social impact assessments covering bias, fairness, privacy, environmental costs, and labor remain uneven. To characterize this landscape, we conduct the first comprehensive analysis of social impact evaluation reporting, examining 186 first-party release reports and 248 third-party evaluation sources, supplemented by devel
The proliferation of foundation models across high-stakes systems necessitates robust evaluation frameworks, bringing the unevenness of social impact assessments to the forefront.
A strategic reader should care because the lack of standardized and comprehensive social impact evaluations for AI poses significant regulatory, reputational, and ethical risks, impeding responsible AI development and deployment.
The focus is shifting from general capability evaluations to a more critical examination of social impact assessments, indicating increasing pressure for accountability from both first and third-party evaluators.
- · AI ethicists and researchers
- · Independent AI safety auditors
- · Regulatory bodies
- · Organizations prioritizing responsible AI development
- · AI developers ignoring social impacts
- · Companies relying on opaque AI systems
- · Consumers affected by biased AI
- · Organizations facing regulatory scrutiny
Increased demand for specialized tools and methodologies for AI social impact assessment.
New regulatory mandates requiring standardized social impact reports for AI system deployment.
The emergence of an 'AI social impact rating' industry influencing investment and adoption decisions.
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