Summary
This paper presents a framework for evaluating the social impacts of generative AI systems across modalities (text, image, audio, video). The framework divides evaluations into two categories: technical base system evaluations and people/society evaluations.
Key Contributions
- Provides a structured framework for evaluating generative AI social impacts
- Identifies 7 key categories for technical base system evaluation
- Outlines 5 broader categories for societal context evaluation
- Offers concrete recommendations for mitigating harms in each category
Framework Categories
Technical Base System
- Bias, stereotypes, and representational harms
- Cultural values and sensitive content
- Disparate performance
- Environmental costs
- Privacy and data protection
- Financial costs
- Data and content moderation labor
People and Society
- Trustworthiness and autonomy
- Inequality, marginalization, and violence
- Concentration of authority
- Labor and creativity
- Ecosystem and environment
Key Insights
- Framework emphasizes evaluating both technical components and societal context
- Highlights importance of considering intersectional impacts
- Notes current evaluation landscape requires more investment
- Technical evaluations alone cannot justify rights-violating applications
- No universal consensus on what constitutes social impacts or how to evaluate them
Limitations
- Evaluations are bounded by current understanding and available methods
- Many evaluations focus on English language and Western contexts
- Hard to standardize evaluations due to evolving nature of impacts
- Difficult to evaluate long-term societal effects
- Protected class categorization cannot be exhaustive
Implications
- Need for more standardized evaluation approaches
- Importance of considering both technical and social dimensions
- Critical to involve diverse stakeholders in evaluation process
- Evaluations should inform appropriate use contexts
- Regular updates needed as technology and society evolve
Personal Notes
Framework provides comprehensive starting point for evaluating generative AI impacts but highlights many open challenges in measurement and standardization. Emphasizes need for both technical rigor and societal context in evaluations.