当人机预测误差负相关时，可构建稳定提升决策效用的互补策略。
Robust Human-AI Complementarity under Uncertainty
- 通过分析人机预测误差的相关性结构，发现负相关时能实现互补价值
- 实证验证在真实预测任务中存在负相关误差，支持互补性条件
- 适合关注人机协同决策优化的研究者与应用开发者
机器学习模型常旨在增强而非取代人类决策者，提供与人类判断互补的信息。然而实践中，即便模型提供有效信号，人类决策者也常无法实现这种互补收益。本文研究了人类与人工智能在信息质量认知不对称下，提取互补价值的能力。结果表明，人机预测误差之间的相关结构是关键因素。当人工智能预测误差与人类预测误差呈负相关时，决策者可构建稳健策略，确保期望效用提升。我们通过真实世界预测基准进行了实证研究，检验了互补性条件在实际中的出现情况。
原文摘要 · Abstract (English)
Machine learning models are often intended to augment rather than replace human decision makers, by providing information that is complementary to human judgement. Yet, in practice, human decision makers routinely fail to realize such complementary gains, even when models provide useful signal. In this work, we study how asymmetric information about the quality of information available to a human decision maker vs. an AI impacts the ability of a decision maker to extract complementary value from AI predictions. We show that a key factor is the error correlation structure between human and AI predictions. In particular, when the AI's prediction errors are \textit{negatively correlated} with those of the human, the decision maker can construct robust strategies which guarantee improvements in expected utility. We empirically investigate whether these conditions for complementarity arise in practice, using real-world forecasting benchmarks.
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