Tootfinder

Toward AI Matching Policies in Homeless Services: A Qualitative Study with Policymakers
Caroline M. Johnston, Olga Koumoundouros, Angel Hsing-Chi Hwang, Laura Onasch-Vera, Eric Rice, Phebe Vayanos
https://arxiv.org/abs/2508.07129

Toward AI Matching Policies in Homeless Services: A Qualitative Study with Policymakers
Artificial intelligence researchers have proposed various data-driven algorithms to improve the processes that match individuals experiencing homelessness to scarce housing resources. It remains unclear whether and how these algorithms are received or adopted by practitioners and what their corresponding consequences are. Through semi-structured interviews with 13 policymakers in homeless services in Los Angeles, we investigate whether such change-makers are open to the idea of integrating AI i…

Measuring and mitigating overreliance is necessary for building human-compatible AI
Lujain Ibrahim, Katherine M. Collins, Sunnie S. Y. Kim, Anka Reuel, Max Lamparth, Kevin Feng, Lama Ahmad, Prajna Soni, Alia El Kattan, Merlin Stein, Siddharth Swaroop, Ilia Sucholutsky, Andrew Strait, Q. Vera Liao, Umang Bhatt
https://arxiv.org/abs/2509.080…

Measuring and mitigating overreliance is necessary for building human-compatible AI
Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative "thought partners," capable of engaging more fluidly in natural language. As LLMs increasingly influence consequential decisions across diverse domains from healthcare to personal advice, the risk of overreliance - relying on LLMs beyond their capabilities - grows. This position paper argues that measuring and mitigating overreliance must become central to LLM research and deployment. …