When machine-learning algorithms are deployed in high-stakes decisions, we want to ensure that their deployment leads to fair and equitable outcomes. This concern has motivated a fastgrowing literature that focuses on diagnosing and addressing disparities in machine predictions. However, many machine predictions are deployed to assist in decisions where a human decisionmaker retains the ultimate decision authority. In this article, we therefore consider how properties of machine predictions affect the resulting human decisions. We show in a formal model that the inclusion of a biased human decision-maker can revert common relationships between the structure of the algorithm and the qualities of resulting decisions. Specifically, we document that excluding information about protected groups from the prediction may fail to reduce, and may even increase, ultimate disparities. While our concrete results rely on specific assumptions about the data, algorithm, and decision-maker, they show more broadly that any study of critical properties of complex decision systems, such as the fairness of machine-assisted human decisions, should go beyond focusing on the underlying algorithmic predictions in isolation.
Putting Disclosure to the Test of financial transactions. Moreover, the testing method mistakenly assumes a direct link between comprehension and improved decisions, and so erroneously uses comprehension tests. As disclosure becomes more central to people's daily lives, from medical decision aids to nutritional labels, greater attention should be given to the testing policies that justify their implementation. This article proposes several ways to improve the content and design of quantitative studies as we enter the era of testing.
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