JSAI2025

Presentation information

Organized Session

Organized Session » OS-14

[4M1-OS-14a] OS-14

Fri. May 30, 2025 9:00 AM - 10:40 AM Room M (Room 1008)

オーガナイザ:福地 庸介(東京都立大学),前川 知行(静岡大学),寺田 和憲(岐阜大学),山田 誠二(国立情報学研究所),今井 倫太(慶應義塾大学)

9:20 AM - 9:40 AM

[4M1-OS-14a-02] SVM-based Cognitive and AI Performance Models for Trust Calibration AI

〇Takumi Tsujiyama1, Seiji Yamada2, Takashi Onoda1 (1. Aoyama Gakuin University, 2. National Institute of Informatics)

Keywords:AI, human–AI cooperative decision making, trust calibration

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.

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