2 papers
cs.CY2026
PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm
Jing-Jing Li, Joel Mire, Eve Fleisig +4
Current AI safety frameworks, which often treat harmfulness as binary, lack the flexibility to handle borderline cases where humans meaningfully disagree. To build more pluralistic…
cs.CL2025
SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior
Jing-Jing Li, Valentina Pyatkin, Max Kleiman-Weiner +7
The ideal AI safety moderation system would be both structurally interpretable (so its decisions can be reliably explained) and steerable (to align to safety standards and reflect…