Enhancing the Accuracy and Fairness of Human Decision Making
Enhancing the Accuracy and Fairness of Human Decision Making
复制标题
提高人类决策的准确性和公平性
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Manuel Gomez Rodriguez
中科院分区：
文献类型：
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作者：
Isabel Valera;A. Singla;Manuel Gomez Rodriguez
社会经常依赖人类专家做出影响其成员的各种决定，从法官做出的监禁或释放决定、警察做出的拦截搜身决定到学者做出的接受或拒绝决定。在这种情况下，每项决策均由专家做出，该专家通常是从专家库中统一随机选出的。然而，由于经验有限、隐含偏见或错误的概率推理，这些决策可能是不完美的。能否通过优化专家分配和决策来提高整个决策过程的准确性和公平性？在本文中，我们从顺序决策的角度解决了上述问题，并表明，对于文献中的不同公平概念，它可以简化为一系列（约束）加权二分匹配，可以使用具有近似保证的算法有效地解决该问题。此外，这些算法还受益于后验采样，以主动权衡利用（选择导致准确和公平决策的专家任务）和探索（选择专家任务以了解专家的偏好和偏见）。我们展示了我们的算法在合成数据和真实数据上的有效性，并表明它们可以显着提高专家库做出的决策的准确性和公平性。
Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics. In this context, each decision is taken by an expert who is typically chosen uniformly at random from a pool of experts. However, these decisions may be imperfect due to limited experience, implicit biases, or faulty probabilistic reasoning. Can we improve the accuracy and fairness of the overall decision making process by optimizing the assignment between experts and decisions? In this paper, we address the above problem from the perspective of sequential decision making and show that, for different fairness notions from the literature, it reduces to a sequence of (constrained) weighted bipartite matchings, which can be solved efficiently using algorithms with approximation guarantees. Moreover, these algorithms also benefit from posterior sampling to actively trade off exploitation---selecting expert assignments which lead to accurate and fair decisions---and exploration---selecting expert assignments to learn about the experts' preferences and biases. We demonstrate the effectiveness of our algorithms on both synthetic and real-world data and show that they can significantly improve both the accuracy and fairness of the decisions taken by pools of experts.