Unexploited Information Value in Human-AI Collaboration
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912244155023360 |
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| author | Guo, Ziyang Wu, Yifan Hartline, Jason Hullman, Jessica |
| author_facet | Guo, Ziyang Wu, Yifan Hartline, Jason Hullman, Jessica |
| contents | Humans and AIs are often paired on decision tasks with the expectation of achieving complementary performance -- where the combination of human and AI outperforms either one alone. However, how to improve performance of a human-AI team is often not clear without knowing more about what particular information and strategies each agent employs. In this paper, we propose a model based in statistical decision theory to analyze human-AI collaboration from the perspective of what information could be used to improve a human or AI decision. We demonstrate our model on a deepfake detection task to investigate seven video-level features by their unexploited value of information. We compare the human alone, AI alone and human-AI team and offer insights on how the AI assistance impacts people's usage of the information and what information that the AI exploits well might be useful for improving human decisions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_10463 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unexploited Information Value in Human-AI Collaboration Guo, Ziyang Wu, Yifan Hartline, Jason Hullman, Jessica Human-Computer Interaction Artificial Intelligence Humans and AIs are often paired on decision tasks with the expectation of achieving complementary performance -- where the combination of human and AI outperforms either one alone. However, how to improve performance of a human-AI team is often not clear without knowing more about what particular information and strategies each agent employs. In this paper, we propose a model based in statistical decision theory to analyze human-AI collaboration from the perspective of what information could be used to improve a human or AI decision. We demonstrate our model on a deepfake detection task to investigate seven video-level features by their unexploited value of information. We compare the human alone, AI alone and human-AI team and offer insights on how the AI assistance impacts people's usage of the information and what information that the AI exploits well might be useful for improving human decisions. |
| title | Unexploited Information Value in Human-AI Collaboration |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2411.10463 |