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Abstract

Mental health care around the globe faces a bottleneck. While the prevalence of mental health disorders continues to increase, the supply of mental health resources fails to grow at a comparative rate. The result is often long waiting times to obtain psychiatric care, and a widening gap between who needs treatment and who has access to it. Artificial intelligence (AI) is often touted as a solution by identifying high-risk patient groups, reallocating resources, predicting adverse events, and enhancing clinical documentation via AI ‘scribes.’ In reality, these AI implementations learn societal biases from their training data, thus they risk perpetuating or even amplifying existing inequities in mental healthcare. Much of my work is propelled by the question: “If we were to implement this AI tool today, would it perpetuate existing disparities in mental healthcare?” To this end, I study diagnostic inequities, examine how bias enters AI systems, test how variable selection shapes model behavior, and evaluate models with fairness metrics. In this talk, I’ll share insights from projects using New York City electronic health records and nationwide Danish registry data.

Presenter bio

Alissa Valentine is a she/they queer scientist who studies disparities in psychiatry, with the goal of decreasing stigma and revealing who is being failed by existing psychiatric frameworks. Alissa is currently a postdoc at Copenhagen University on the TRUSTMIND team — a collaborative research project aimed to evaluate the lack of trustworthiness in AI models for depression. Alissa holds a PhD in Neuroscience from Mount Sinai Health System in New York City, a MS in Bioinformatics from Northeastern University in Boston, and BS from Haverford College in Philadelphia. Alissa is also a core organizer at Queer in AI, an international organization aimed at elevating queer research and advocating for better AI policy. In their free time this summer, Alissa ate lots of ice cream, danced to Bad Bunny, and swam with their friends.