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Bridging fair-aware artificial intelligence and co-creation for equitable mental healthcare

Nature Reviews Psychology (2025)Cite this article

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Abstract

Artificial intelligence (AI) holds immense potential to provide scalable, personalized and accessible solutions to mental healthcare. However, biases in AI systems might exacerbate current mental healthcare disparities, particularly for minoritized populations. In this Perspective, we introduce a model for bias reduction and inclusion through dynamic generative equity (adaptive AI), which has been designed to prioritize equity throughout the development and implementation of AI systems in mental health interventions. This model integrates fair-aware machine learning with co-creation techniques, combining quantitative methodologies to detect bias in AI algorithms with qualitative input from community collaborators to ensure cultural relevance and practical application. We describe the model’s procedures and iterative feedback loops, which ensure that AI-based interventions are culturally responsive and evolve dynamically with real-time feedback. We also discuss the model’s potential applications, current limitations and areas for future research.

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Fig. 1: Pathways through which structural inequities contribute to biased AI-based mental health interventions.
Fig. 2: Integration of fair-aware and co-creation frameworks across the intervention development lifecycle.
Fig. 3: Dual-loop system of the model for bias reduction and inclusion through dynamic generative equity.

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Authors and Affiliations

  1. Technological Interventions for Ecological Systems (TIES) Lab, Psychology, The University of Texas at Austin, Austin, TX, USA

    Adela C. Timmons, Jacqueline B. Duong, Sierra N. Walters, Kayla E. Carta, Grace A. Jumonville, Alyssa S. Carrasco, Daniela N. Romero & Ishita P. Khurd

  2. Colliga Apps, Austin, TX, USA

    Adela C. Timmons & Matthew W. Ahle

  3. Mental health Interventions and Novel Therapeutics (MINT) Lab, Psychology, Florida International University, Miami, FL, USA

    Jonathan S. Comer

  4. Human Bio-Behavioral Signals (HUBBS) Lab, Computer Science, University of Colorado Boulder, CO, USA

    Theodora Chaspari

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  1. Adela C. Timmons
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  2. Jacqueline B. Duong
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Contributions

A.C.T. conceptualized and led the writing of the article. J.B.D., S.N.W., K.E.C., G.A.J., A.S.C., D.N.R., M.W.A., J.S.C., I.P.K. and T.C. contributed to writing specific sections, editing and revising content. All authors reviewed and approved the final manuscript.

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Correspondence to Adela C. Timmons.

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Competing interests

A.C.T. and M.W.A. own intellectual property and stock in Colliga Apps and could benefit financially from the commercialization of related research. J.S.C. earns textbook royalties from Macmillan Learning and an editorial stipend from the Association for Behavioral and Cognitive Therapies for projects unrelated to the present work.

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Nature Reviews Psychology thanks Gabriela A. Nagy, Nelson Shen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Timmons, A.C., Duong, J.B., Walters, S.N. et al. Bridging fair-aware artificial intelligence and co-creation for equitable mental healthcare. Nat Rev Psychol (2025). https://doi.org/10.1038/s44159-025-00491-5

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