Bridging Fair-Aware AI and Co-Creation Frameworks for Equitable Mental Health

Artificial intelligence (AI) holds immense potential to revolutionize mental health care by providing scalable, personalized, and accessible solutions. However, systemic biases in AI models pose a significant risk of exacerbating disparities, particularly for minoritized populations, underscoring the critical need for robust frameworks that prioritize equity throughout development and implementation. This Perspective introduces the Bias Reduction and Inclusion through Dynamic Generative Equity (BRIDGE) Model, an innovative framework designed to address these complexities. The BRIDGE Model integrates fair-aware machine learning techniques with co-creation methods, combining quantitative approaches to detect bias in algorithms with qualitative input from stakeholders to ensure cultural relevance and practical application. By leveraging dual-level, iterative feedback loops, the BRIDGE Model establishes a systematic and dynamic process for developing equitable AI systems that align technical rigor with real-world contexts. The TIES Parenting Program, an AI-powered digital intervention for child mental health, is presented as a case study to illustrate how this framework is being applied to address real-world challenges. By bridging technical precision with lived experiences, the BRIDGE Model aspires to foster the creation of equitable, adaptive, and culturally responsive AI systems that advance accessibility, trust, and fairness in mental health care.