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A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health
Open accessAbstract
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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“Biases in LLM-based chatbots may perpetuate social inequities, potentially harming underrepresented or stigmatized groups in a (mental) health context, e.g., through errors in diagnostic classifications, proposals of interventions, or mental health advice56.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [56]
Experimental — accuracy not yet measuredIn the citing paper
“Timmons et al. (2023) proposed a comprehensive framework for assessing and mitigating bias in AI mental health applications, identifying LGBTQ+ people as a sensitive class requiring explicit protection and outlining practical strategies at each stage of model development, from data collection through deployment.”
From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Timmons et al. (2023)
In the citing paper
“When training datasets underrepresent LGBTQ+ people, or represent them primarily through pathologizing or stigmatizing framings, the resulting systems will reproduce and amplify those biases at scale (Timmons et al., 2023).”
From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Timmons et al., 2023
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Metadata
- 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
- 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
- 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
- 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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