WinoReferral: A Benchmark to Evaluate LLM Responses to Prompts Implying Mental Health Disorders
About
The use of LLMs has contributed to serious mental health conditions among a subset of users, most alarmingly cases of delusional thinking and suicide. My talk will begin by surveying leading theories from psychology and human computer interaction that explain how LLM use interacts with user mental health. I will then present my research on evaluating LLM responses to prompts that imply a user has depression and discuss our current results, which indicate high variance in AI companies’ safety policies for referring users to mental health professionals. I will conclude by proposing policy changes that could limit the harms of LLMs on user mental health.
Speaker
Micah Benson
Micah studies the societal impacts of large language models (LLMs) as a PhD Student at Boston University's Faculty of Computing & Data Sciences. He uses interpretability methods to investigate how LLMs represent social concepts such as identity and politics, with the goal of developing techniques to improve model fairness. He also conducts audits that simulate new uses of LLMs to analyze potential benefits and risks of the technology. Before BU, Micah graduated from WashU with a double major in data science and English.