Multi-Modal Machine Learning for Early Trust Prediction in Human-AI Interaction Using Face Image and GSR Bio Signals
It addresses the need for safe AI integration in healthcare, specifically for mental health applications like ADHD mHealth, by enabling adaptive AI systems to maintain calibrated trust, though it is incremental as it builds on existing multi-modal and bio-signal methods.
This study tackled the problem of predicting human trust in AI systems by proposing a multi-modal machine learning framework that combines facial image and galvanic skin response (GSR) data, achieving an accuracy of 0.83 and F1-score of 0.88 in early detection windows.
Predicting human trust in AI systems is crucial for safe integration of AI-based decision support tools, especially in healthcare. This study proposes a multi-modal machine learning framework that combines image and galvanic skin response (GSR) data to predict early user trust in AI- or human-generated recommendations in a simulated ADHD mHealth context. Facial video data were processed using OpenCV for frame extraction and transferred learning with a pre-trained transformer model to derive emotional features. Concurrently, GSR signals were decomposed into tonic and phasic components to capture physiological arousal patterns. Two temporal windows were defined for trust prediction: the Early Detection Window (6 to 3 seconds before decision-making) and the Proximal Detection Window (3 to 0 seconds before decision-making). For each window, trust prediction was conducted separately using image-based, GSR-based, and multimodal (image + GSR) features. Each modality was analyzed using machine learning algorithms, and the top-performing unimodal models were integrated through a multimodal stacking ensemble for final prediction. Experimental results showed that combining facial and physiological cues significantly improved prediction performance. The multimodal stacking framework achieved an accuracy of 0.83, F1-score of 0.88, and ROC-AUC of 0.87 in the Early Detection Window, and an accuracy of 0.75, F1-score of 0.82, and ROC-AUC of 0.66 in the Proximal Detection Window. These results demonstrate the potential of bio signals as real-time, objective markers of user trust, enabling adaptive AI systems that dynamically adjust their responses to maintain calibrated trust which is a critical capability in mental health applications where mis-calibrated trust can affect diagnostic and treatment outcomes.