分析人机协作中未被利用的信息价值，提升联合决策性能。
Unexploited Information Value in Human-AI Collaboration
- 基于统计决策理论建模，识别可提升判断的信息
- 在深度伪造检测中验证七种视频特征的未利用价值
- 揭示AI如何影响人类信息使用，指导优化协作策略
人类与人工智能常被配对执行决策任务，预期实现互补性表现——人机组合优于单独个体。然而，在不了解双方具体使用信息和策略的情况下，如何提升人机团队性能尚不明确。本文提出一种基于统计决策理论的模型，从信息利用角度分析人机协作潜力。我们在深度伪造检测任务中，评估七种视频级特征的未被利用信息价值。通过比较人类单独、AI单独及人机协同的表现，揭示了AI辅助如何影响人类对信息的使用方式，并指出AI有效利用的信息可能对改进人类决策具有重要参考价值。
原文摘要 · Abstract (English)
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.
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