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arXiv AI By Huy Nguyen, Yue Lin

Is Your Neighborhood Safe? Place-based Stigma in Large Language Models' Urban Safety Judgments

Read the original on arXiv AI →

Large language models (LLMs) are increasingly used to guide urban safety decisions, but this study shows that their judgments are more influenced by neighborhood names than by geographic coordinates. Across seven instruct‑tuned models tested on 186 neighborhoods in Los Angeles and Chicago, name‑based ratings varied significantly and correlated with the proportion of locally dominant marginalized groups, while coordinate‑only ratings remained largely flat. The research finds that removing neighborhood names reduces both bias and accuracy, highlighting the complex role of demographic stereotypes and crime signals in LLM safety assessments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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