Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
Fairness-aware machine learning: practical challenges and lessons learned
Bird S., Hutchinson B., Kenthapadi K., K c man E., Mitchell M.  WWW 2019 (Companion Proceedings of The 2019 World Wide Web Conference, San Francisco, CA,  May 13-17, 2019) 1297-1298. 2019. Type: Proceedings
Date Reviewed: May 7 2020

This is a timely paper in light of recent stories about bias in artificial intelligence (AI) systems, such as the COMPAS system used in Florida to predict recidivism. The tutorial’s aim is to describe what the authors call a “fairness-first approach” to machine learning. This is similar to a security-first view of system construction, that is, fairness should be built in from the start rather than bolted on later. The tutorial covers industry best practices, sources of bias, algorithmic techniques for fairness, and fairness methods in practice. The notion of fairness is discussed along with various definitions, for example, individual and group fairness. This includes fairness in ranking users for things like credit offers.

A bibliography provides further references on this important topic. For those who missed the conference tutorial, and because this paper is just a brief invitation to it, readers should refer to the papers listed in the bibliography for more comprehensive introductions to the topic of fairness in machine learning.

Reviewer:  J. P. E. Hodgson Review #: CR146963
Bookmark and Share
  Reviewer Selected
Editor Recommended
Featured Reviewer
 
Would you recommend this review?
yes
no

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright © 2000-2020 ThinkLoud, Inc.
Terms of Use
| Privacy Policy