Interpretable but Not Necessarily Meaningful: Language, Voice, and Clinical Inference in AI-Based Mental Health Prediction-A Commentary on Wang and Sambamoorthi (2026).
An interpretable and fairness-aware Random Forest model that predicts mental health disorders using acoustic features derived from the Bridge2AI-Voice dataset is developed, and the demonstration of strong gender fairness represents a commendable step toward responsible artificial intelligence in voice science.