Incomprehensible Discrimination
By James Grimmelmann, D. Westreich. 2017
The following (fictional) opinion of the (fictional) Zootopia Supreme Court of the (fictional) State of Zootopia is designed to highlight one particularly interesting issue raised by Solon Barocas and Andrew Selbst in Big Data’s Disparate Impact. Their article discusses many ways in which data-intensive algorithmic methods can go wrong when they are used to make employment and other sensitive decisions. Our vignette deals with one in particular: the use of algorithmically derived models that are both predictive of a legitimate goal and have a disparate impact on some individuals. Like Barocas and Selbst, we think it raises fundamental questions about how antidiscrimination law works and about what it ought to do. But we are perhaps slightly more optimistic than they are that the law already has the doctrinal tools it needs to deal appropriately with cases of this sort.
How researchers see this paper
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- Zachary Chase Lipton
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- Yongfeng Zhang
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- Adrian Weller
Citations in the corpus
- Does mitigating ML's disparate impact require disparate treatment? (2017, background): “A legal opinion by Grimmelmann andWestreich supports this view [GW17]:”