Designing Algorithmic Recommendations to Achieve Human-AI Complementarity
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为实现人机互补而设计算法建议
- 研究问题
- 预测准确的算法未必改善人的决策；研究如何把建议对人类二元处理选择的影响纳入算法设计，以实现人机互补。
- 方法与思路
- 作者用潜在结果框架刻画建议效应，在单调性假设下分解遵从与无建议时的主动选择，并推导可由机器学习实现的建议算法。
- 材料陈述的贡献
- 摘要称，该框架有助于解释模拟招聘实验中不同算法的相对表现，并导出可利用有限训练数据实现的极小极大最优建议算法。
- 阅读关注点
- 应核对单调性如何限制人的反应、实验如何识别建议的影响，以及极小极大准则的损失函数、信息条件和实施要求。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未给出实验效应大小，最优性限于其设定的模型与准则。
原文摘要
Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the design and role of algorithmic assistants becomes particularly concerning in light of empirical evidence that suggests that algorithmic assistants again and again fail to improve human decisions. In this article, we formalize the design of recommendation algorithms that assist human decision-makers without making restrictive ex-ante assumptions about how recommendations affect decisions. We formulate an algorithmic-design problem that leverages the potential-outcomes framework from causal inference to model the effect of recommendations on a human decision-maker's binary treatment choice. Within this model, we introduce a monotonicity assumption that leads to an intuitive classification of human responses to the algorithm. Under this assumption, we can express the human's response to algorithmic recommendations in terms of their compliance with the algorithm and the active decision they would take if the algorithm sends no recommendation. We showcase the utility of our framework using an online experiment that simulates a hiring task. We argue that our approach can make sense of the relative performance of different recommendation algorithms in the experiment and can help design solutions that realize human-AI complementarity. Finally, we leverage our approach to derive minimax optimal recommendation algorithms that can be implemented with machine learning using limited training data.