![[2602.18459] From Bias Mitigation to Bias Negotiation: Governing Identity and Sociocultural Reasoning in Generative AI](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18459] From Bias Mitigation to Bias Negotiation: Governing Identity and Sociocultural Reasoning in Generative AI
Summary
This article discusses the shift from bias mitigation to bias negotiation in generative AI, emphasizing the need for ethical governance of identity in AI systems.
Why It Matters
Understanding bias negotiation is crucial for developing AI systems that not only mitigate identity-related harms but also recognize the positive role of sociocultural reasoning. This approach can enhance model functionality and promote justice in AI applications across diverse cultural contexts.
Key Takeaways
- Bias negotiation offers a framework for regulating identity in AI systems beyond mere mitigation.
- The study identifies key strategies for negotiating identity in AI, including probabilistic framing and harm-value balancing.
- A positive role for sociocultural reasoning is essential for addressing structural inequities in AI applications.
- Bias negotiation requires dynamic evaluation methods rather than static benchmarks.
- The proposed framework aids in systematic test-suite design for assessing bias negotiation capabilities.
Computer Science > Computers and Society arXiv:2602.18459 (cs) [Submitted on 5 Feb 2026] Title:From Bias Mitigation to Bias Negotiation: Governing Identity and Sociocultural Reasoning in Generative AI Authors:Zackary Okun Dunivin, Bingyi Han, John Bollenbocher View a PDF of the paper titled From Bias Mitigation to Bias Negotiation: Governing Identity and Sociocultural Reasoning in Generative AI, by Zackary Okun Dunivin and 2 other authors View PDF HTML (experimental) Abstract:LLMs act in the social world by drawing upon shared cultural patterns to make social situations understandable and actionable. Because identity is often part of the inferential substrate of competent judgment, ethical alignment requires regulating when and how systems invoke identity. Yet the dominant governance regime for identity-related harm remains bias mitigation, which treats identity primarily as a source of measurable disparities or harmful associations to be detected and suppressed. This leaves underspecified a positive, context-sensitive role for identity in interpretation. We call this governance problem bias negotiation: the normative regulation of identity-conditioned judgments of sociocultural relevance, inference, and justification. Empirically, we probe the feasibility of bias negotiation through semi-structured interviews with multiple publicly deployed chatbots. We identify recurring repertoires for negotiating identity including probabilistic framing of group tendencies and harm-val...