Beyond Third-Person Audits: Situated Interaction Auditing for User-Centered LLM Bias Research
arXiv:2606.12247v1 Announce Type: cross Abstract: Research on bias in large language models (LLMs) has predominantly focused on third-person audits, which study how models represent or evaluate demographic groups as external subjects. However, this paradigm overlooks a structural blind spot because the user is absent from the audit. In practice, LLMs are used in open-ended, personal interactions, during which the model implicitly represents the user and adjusts its responses accordingly. When identical requests yield different responses depending on who is asking, bias manifests not in how the
The proliferation of LLMs in diverse user interactions necessitates a more nuanced understanding of bias beyond static, third-person audits, reflecting the dynamic nature of their real-world application.
Understanding how LLMs exhibit bias in situated, personal interactions can deeply influence public trust, regulatory frameworks, and their ethical deployment across sensitive applications.
The focus for mitigating LLM bias shifts from solely auditing static model outputs to also analyzing and addressing how models implicitly represent and adjust to individual users, revealing a more complex and personalized form of bias.
- · Ethical AI researchers
- · Companies prioritizing user-centered AI development
- · Auditing frameworks beyond generic benchmarks
- · LLMs with unmitigated interactional bias
- · Companies relying solely on third-person bias audits
- · Generic, one-size-fits-all AI governance initiatives
Research efforts will increasingly pivot towards understanding and mitigating 'situated interaction bias' in LLMs.
New AI safety and ethical guidelines will emerge, requiring LLM developers to demonstrate fairness in dynamic user interactions, not just static evaluations.
Public perception of LLM fairness will become increasingly tied to personalized experiences, potentially leading to demands for 'explainable and customizable fairness' in AI applications.
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