Building Trust in AI-Driven Mental Health Tools

Abstract

The chapter discusses the challenges of fairness and fairness in AI-driven mental health tools, highlighting the need for societal biases, diverse datasets, and equitable outcomes across different demographic groups. A novel framework is proposed that integrates bias detection, fairness-aware algorithms, and user-centric trust-building mechanisms. This approach is the first comprehensive in this domain, leveraging advanced interpretability techniques and adversarial training to mitigate bias while maintaining model performance. Experimental results show a 40% improvement in fairness metrics and a 30% increase in trust and acceptance of AI-driven mental health tools. This method could set a new benchmark for ethical and inclusive AI solutions.

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