Demographic bias in LLM dispatch decisions appears mainly in ambiguous-severity incidents, varies by language and demographic axis with religious appearance showing the largest effects, and does not transfer consistently across English and Mandarin.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 4roles
background 1polarities
support 1representative citing papers
LLMs produce lower-fidelity summaries of identical public comments when attributed to lower-status occupations like street vendors versus financial analysts, with inconsistent race effects and no gender effects.
Controlled prompt interventions reveal strong affiliation bias in LLM peer reviews favoring top-ranked institutions, plus effects from seniority and publication history.
Weird generalization in fine-tuned models is brittle, appearing only in specific cases and disappearing under prompt-based interventions that make the undesired behavior expected.
citing papers explorer
-
Auditing demographic bias in AI-based emergency police dispatch: a cross-lingual evaluation of eleven large language models
Demographic bias in LLM dispatch decisions appears mainly in ambiguous-severity incidents, varies by language and demographic axis with religious appearance showing the largest effects, and does not transfer consistently across English and Mandarin.
-
All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs?
LLMs produce lower-fidelity summaries of identical public comments when attributed to lower-status occupations like street vendors versus financial analysts, with inconsistent race effects and no gender effects.
-
Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews
Controlled prompt interventions reveal strong affiliation bias in LLM peer reviews favoring top-ranked institutions, plus effects from seniority and publication history.
-
Weird Generalization is Weirdly Brittle
Weird generalization in fine-tuned models is brittle, appearing only in specific cases and disappearing under prompt-based interventions that make the undesired behavior expected.