SVM-based Cognitive and AI Performance Models for Trust Calibration AI

Abstract

If trust in AI breaks down due to over-trust or under-trust by humans, achieving high performance in human-AI collaborative decision-making becomes difficult. To address this issue, the human-AI trust relationship needs to be optimized by adaptively calibrating trust. Against this background, prior research proposed a trust calibration AI that automatically detects over-trust or under-trust and encourages humans to calibrate their trust in AI. This AI requires a cognitive/AI performance model to estimate the problem-solving ability of both humans and AI. However, specific methods to create this model do not exist at present. Therefore, this study proposes a method to construct a cognitive/AI performance model using Support Vector Machine (SVM) classification model. To evaluate the effectiveness of this method, an experiment was conducted using a chest X-ray interpretation task as a case study.