Using artificial intelligence to address mental health inequalities : co-creating machine learning algorithms with key stakeholders and citizen engagement

Morgan, Phil and Cogan, Nicola Ann (2024) Using artificial intelligence to address mental health inequalities : co-creating machine learning algorithms with key stakeholders and citizen engagement. Journal of Public Mental Health. ISSN 1746-5729 (https://doi.org/10.1108/JPMH-07-2024-0095)

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Abstract

Purpose Artificial intelligence (AI) is poised to reshape mental health practices, policies and research in the coming decade. Simultaneously, mental health inequalities persist globally, imposing considerable costs on individuals, communities and economies. This study aims to investigate the impact of AI technologies on future citizenship for individuals with mental health challenges (MHCs). Design/methodology/approach This research used a community-based participatory approach, engaging peer researchers to explore the perspectives of adults with MHCs from a peer-led mental health organisation. This study evaluated potential threats and opportunities presented by AI technologies for future citizenship through a co-created film, depicting a news broadcast set in 2042. Data were gathered via semi-structured interviews and focus groups and were analysed using a reflexive thematic approach. Findings The analysis identified four key themes: Who holds the power? The divide, What it means to be human, and Having a voice. The findings indicate that adults with living experiences of MHCs are eager to influence the development of AI technologies that affect their lives. Participants emphasised the importance of activism and co-production while expressing concerns about further marginalisation. Originality/value This study provides new insights into the intersection of AI, technology and citizenship, highlighting the critical need for inclusive practices in technological advancement. By incorporating the perspectives of individuals with living experiences, this study advocates for participatory approaches in shaping AI technologies in mental health. This includes the co-creation of machine learning algorithms and fostering citizen engagement to ensure that advancements are equitable and empowering for people with MHCs.

ORCID iDs

Morgan, Phil and Cogan, Nicola Ann ORCID logoORCID: https://orcid.org/0000-0003-0861-5133;

Persistent Identifier

https://doi.org/10.17868/strath.00091060
CORE (COnnecting REpositories)