Bridging the Trust Gap: A Framework for Explainable AI in Mental Health Diagnostics

Abstract

The integration of AI into mental health diagnostics offers potential gains in accuracy, scalability, and personalisation. However, clinical adoption remains limited by a trust deficit rooted in model opacity, algorithmic bias, and misalignment with ethical-legal standards. This paper proposes a conceptual framework for Explainable AI (XAI) specifically designed for mental health applications. Through a critical synthesis of literature, we identify three interconnected trust barriers: the black‑box problem, bias propagation from non‑representative datasets, and the lack of actionable explanations for clinicians. Our framework addresses these barriers through four integrated pillars: (1) a hybrid XAI strategy combining post‑hoc (SHAP, Grad‑CAM) and intrinsic (attention) techniques; (2) mandatory evaluation of explanations using faithfulness and stability metrics; (3) embedded, proactive bias mitigation across the AI pipeline; and (4) an ethical‑regulatory scaffold compliant with GDPR, HIPAA, and the EU AI Act, while prioritising clinical workflow integration. The framework shifts the paradigm from accuracy‑first to trust‑first design. It provides a structured blueprint for developing transparent, fair, and clinically actionable AI systems, thereby bridging the explainability gap and laying the conceptual foundation for trustworthy AI‑augmented mental health care.