Algorithmic Decision Making and the Cost of Fairness
data_society's bookmarks 2019-08-20
Type
Conference Paper
Author
Sam Corbett-Davies
Author
Emma Pierson
Author
Avi Feller
Author
Sharad Goel
Author
Aziz Huq
URL
http://doi.acm.org/10.1145/3097983.3098095
Series
KDD '17
Place
New York, NY, USA
Publisher
ACM
Pages
797–806
ISBN
978-1-4503-4887-4
Date
2017
Extra
event-place: Halifax, NS, Canada
DOI
10.1145/3097983.3098095
Accessed
2019-06-21 16:39:48
Library Catalog
ACM Digital Library
Abstract
Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are substantially more likely than white defendants to be incorrectly classified as high risk. To mitigate such disparities, several techniques have recently been proposed to achieve algorithmic fairness. Here we reformulate algorithmic fairness as constrained optimization: the objective is to maximize public safety while satisfying formal fairness constraints designed to reduce racial disparities. We show that for several past definitions of fairness, the optimal algorithms that result require detaining defendants above race-specific risk thresholds. We further show that the optimal unconstrained algorithm requires applying a single, uniform threshold to all defendants. The unconstrained algorithm thus maximizes public safety while also satisfying one important understanding of equality: that all individuals are held to the same standard, irrespective of race. Because the optimal constrained and unconstrained algorithms generally differ, there is tension between improving public safety and satisfying prevailing notions of algorithmic fairness. By examining data from Broward County, Florida, we show that this trade-off can be large in practice. We focus on algorithms for pretrial release decisions, but the principles we discuss apply to other domains, and also to human decision makers carrying out structured decision rules.
Proceedings Title
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining