| Session | ||
SE6 - BO2: Privacy and fairness in behavioral operations
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| Presentations | ||
On the fairness of machine-assisted human decisions 1Stanford University; 2Columbia University Typically, fairness properties of algorithmic decisions are analyzed as if the machine predictions were implemented directly. However, many machine predictions are instead deployed to assist a human decision-maker who retains the ultimate decision authority. In this article, we therefore consider how properties of machine predictions affect the resulting human decisions through both a formal model and lab experiment. Towards understanding the causes of developing biased algorithms by programmers University of Calgary, Haskayne School of Business, Canada The use of algorithms may be preferred over human judgment; however, algorithms are prone to bias. In this study, we designed a behavioral lab experiment environment to imitate the processes instated by the companies when asking their programmers to develop an ML algorithm for any given task. We investigate the causes of programmers developing biased ML algorithms and study how these causes can be mitigated through policies to encourage the development of fair and less-biased ML algorithms. How good are privacy guarantees? Data sharing, privacy preservation, and platform behavior 1MIT; 2Duke University,; 3University of Toronto; 4Boston College TBD | ||