Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level

Excerpt

arXiv:2608.14692v1 Announce Type: cross Abstract: Personalized, generative AI systems increasingly adapt their behavior to individual users over time, fundamentally changing model behavior. While existing auditing approaches have been effective at surfacing harms in non-personalized contexts, they often rely on static, simulated evaluations and definitions of harm that aggregate across broad, group categories. In this position paper, we argue that such approaches can fail to capture emergent har