Proposes affective safety as a distinct class of AI harms with a taxonomy of self-alienation, bias, and relational harms, arguing that existing safety frameworks address it narrowly or not at all and calling for dedicated approaches focused on cumulative and identity-level effects.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Systematic evaluation shows LLMs frequently give unsafe responses to eating disorder prompts when linguistic cues signal risk, as measured by varying prompt danger levels with clinician feedback.
citing papers explorer
-
Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback
Systematic evaluation shows LLMs frequently give unsafe responses to eating disorder prompts when linguistic cues signal risk, as measured by varying prompt danger levels with clinician feedback.