The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Making
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915162661847040 |
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| author | Takayanagi, Takehiro Hashimoto, Ryuji Chen, Chung-Chi Izumi, Kiyoshi |
| author_facet | Takayanagi, Takehiro Hashimoto, Ryuji Chen, Chung-Chi Izumi, Kiyoshi |
| contents | In AI-assisted decision-making, it is crucial but challenging for humans to appropriately rely on AI, especially in high-stakes domains such as finance and healthcare. This paper addresses this problem from a human-centered perspective by presenting an intervention for self-confidence shaping, designed to calibrate self-confidence at a targeted level. We first demonstrate the impact of self-confidence shaping by quantifying the upper-bound improvement in human-AI team performance. Our behavioral experiments with 121 participants show that self-confidence shaping can improve human-AI team performance by nearly 50% by mitigating both over- and under-reliance on AI. We then introduce a self-confidence prediction task to identify when our intervention is needed. Our results show that simple machine-learning models achieve 67% accuracy in predicting self-confidence. We further illustrate the feasibility of such interventions. The observed relationship between sentiment and self-confidence suggests that modifying sentiment could be a viable strategy for shaping self-confidence. Finally, we outline future research directions to support the deployment of self-confidence shaping in a real-world scenario for effective human-AI collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Making Takayanagi, Takehiro Hashimoto, Ryuji Chen, Chung-Chi Izumi, Kiyoshi Human-Computer Interaction Computation and Language Computers and Society In AI-assisted decision-making, it is crucial but challenging for humans to appropriately rely on AI, especially in high-stakes domains such as finance and healthcare. This paper addresses this problem from a human-centered perspective by presenting an intervention for self-confidence shaping, designed to calibrate self-confidence at a targeted level. We first demonstrate the impact of self-confidence shaping by quantifying the upper-bound improvement in human-AI team performance. Our behavioral experiments with 121 participants show that self-confidence shaping can improve human-AI team performance by nearly 50% by mitigating both over- and under-reliance on AI. We then introduce a self-confidence prediction task to identify when our intervention is needed. Our results show that simple machine-learning models achieve 67% accuracy in predicting self-confidence. We further illustrate the feasibility of such interventions. The observed relationship between sentiment and self-confidence suggests that modifying sentiment could be a viable strategy for shaping self-confidence. Finally, we outline future research directions to support the deployment of self-confidence shaping in a real-world scenario for effective human-AI collaboration. |
| title | The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Making |
| topic | Human-Computer Interaction Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2502.14311 |