让AI只做最擅长的事，人类接手其余，提升匹配决策效果。
Towards Human-AI Complementarity in Matching Tasks
- AI只处理自己最确信的匹配项，其余交给人类判断。
- 800人实验显示，协同方案比纯人工或纯算法效果更好。
- 适合医疗、社服等高风险决策场景使用。
数据驱动的算法匹配系统旨在帮助人类决策者在医疗、社会服务等高风险领域做出更优匹配决策。然而，现有系统未实现人机互补：使用系统的人员决策未必优于单独的人类或算法。本文提出协同匹配（comatch）系统，采用协作模式——不包揽所有决策，而是仅对自身最自信的匹配项作出决定，其余交由人类处理。该系统通过优化自主决策与移交人类的平衡，可理论保证性能最大化。我们开展了大规模真人实验，共800名参与者。结果表明，comatch生成的匹配结果显著优于纯人类或纯算法的表现。实验数据及系统实现已开源，地址见https://github.com/Networks-Learning/human-AI-complementarity-matching。
原文摘要 · Abstract (English)
Data-driven algorithmic matching systems promise to help human decision makers make better matching decisions in a wide variety of high-stakes application domains, such as healthcare and social service provision. However, existing systems are not designed to achieve human-AI complementarity: decisions made by a human using an algorithmic matching system are not necessarily better than those made by the human or by the algorithm alone. Our work aims to address this gap. To this end, we propose collaborative matching (comatch), a data-driven algorithmic matching system that takes a collaborative approach: rather than making all the matching decisions for a matching task like existing systems, it selects only the decisions that it is the most confident in, deferring the rest to the human decision maker. In the process, comatch optimizes how many decisions it makes and how many it defers to the human decision maker to provably maximize performance. We conduct a large-scale human subject study with $800$ participants to validate the proposed approach. The results demonstrate that the matching outcomes produced by comatch outperform those generated by either human participants or by algorithmic matching on their own. The data gathered in our human subject study and an implementation of our system are available as open source at https://github.com/Networks-Learning/human-AI-complementarity-matching.
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