Large language models (LLMs) are increasingly used to generate media, but whether their content perpetuates gender stereotypes is unknown: standard benchmarks rely on selection-based formats rather than long-form generation, and surveyed baselines for local gender associations are scarce outside the West. We collect ge...
Sharif Kazemi, Tanya Popli, Neil K. R. Sehgal et al.· 0 citations
Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., `do not steal'). However, social intelligence depends not only on norm recognition, but also on anticipating who will enforce it and how (e.g., public shame or even impr...
Sunny Rai, Jin-Yi Kuang, Reyhan Jamalova et al.· 0 citations
Online hate speech is associated with harms ranging from deteriorating mental health to violence, yet how consistently platforms moderate hate, and whether enforcement is feasible at scale, remain poorly understood. We audit hate speech moderation on Twitter (now X) using 540,000 tweets annotated by trained native spea...
Manuel Tonneau, D. Thurgood, Diyi Liu et al.· arXiv.org· 2 citations
It is suggested that demographic conditioning in LLMs is not a cue-invariant category-level parameter but depends fundamentally on how identity is cued, reflecting responses to linguistic signals rather than stable demographic categories.
Manuel Tonneau, Neil K. R. Seghal, Niyati Malhotra et al.· 4 citations
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