ロボットミッションにおける人間とAIの共同推論の強化：信頼度に基づくアプローチ
Enhancing Joint Human-AI Inference in Robot Missions: A Confidence-Based Approach
ロボット遠隔操作タスクにおいて、人間とAIの推論のうち信頼度が高い方を選択する共同推論手法をユーザー研究で検証し、AIの信頼度較正が性能に与える影響を明らかにした。
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著者: Duc-An Nguyen, Clara Colombatto, Steve Fleming, Ingmar Posner, Nick Hawes, Raunak Bhattacharyya
分類: cs.HC, cs.RO
原文アブストラクト
Joint human-AI inference holds immense potential to improve outcomes in human-supervised robot missions. Current day missions are generally in the AI-assisted setting, where the human operator makes the final inference based on the AI recommendation. However, due to failures in human judgement on when to accept or reject the AI recommendation, complementarity is rarely achieved. We investigate joint human-AI inference where the inference made with higher confidence is selected. Through a user study with N=100 participants on a representative simulated robot teleoperation task, specifically studying the inference of robots' control delays we show that: a) Joint inference accuracy is higher and its extent is regulated by the confidence calibration of the AI agent, and b) Humans change their inferences based on AI recommendations and the extent and direction of this change is also regulated by the confidence calibration of the AI agent. Interestingly, our results show that pairing poorly-calibrated AI-DSS with humans hurts performance instead of helping the team, reiterating the need for AI-based decision support systems with good metacognitive sensitivity. To the best of our knowledge, our study presents the first application of a maximum-confidence-based heuristic for joint human-AI inference within a simulated robot teleoperation task.
