Cited by 265Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

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A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

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Timmons, Adela C., Duong, Jacqueline B., Simo Fiallo, Natalia, Lee, Theodore, Vo, Huong Phuc Quynh, Ahle, Matthew W., Comer, Jonathan S., Brewer, LaPrincess C., Frazier, Stacy L., Chaspari, Theodora·2023·Perspectives on Psychological Science

DOI 10.1177/17456916221134490PMC PMC10250563

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Abstract

Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.

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Authors: Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T.
Affiliations: The University of Texas at Austin (US)
Department of Psychology, Florida International University
Florida International University (US)
Texas A&M University (US)
Colliga Apps Corporation, Austin, Texas
Mayo Clinic (US)
Corresponding author: Timmons, Adela C.
ORCID: Timmons, Adela C.
Duong, Jacqueline B.
Lee, Theodore
Comer, Jonathan S.
Brewer, LaPrincess C.
Frazier, Stacy L.
Chaspari, Theodora
Venue: Perspectives on Psychological Science
Publisher: SAGE Publications
Published: 2022-12-09
Volume: 18
Issue: 5
Pages: 1062-1096
ISSN: 1745-6916 (ISSN-L)
1745-6924
DOI: 10.1177/17456916221134490
PMC: PMC10250563
OpenAlex: W4311026115
Cited by: 263
References: 226
Citations by year: 2026: 82 · 2025: 107 · 2024: 61 · 2023: 11 · 2022: 1
FWCI: 52.21
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.sagepub.com/licence-information-for-chorus (vor, 365 d)
Locations: Perspectives on Psychological Science (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of Mental Health (R42MH123368)
National Science Foundation (1930019, DGE-0937362)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: algorithmic bias, artificial intelligence in mental health, health equity, mental health disparities, bias mitigation, algorithmic fairness, access to mental health care
MeSH terms: Artificial Intelligence, Awareness, Humans, Mental Health, Technology, Bias

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A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

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