A Framework for Fairer Machine Learning in Organizations

35 Pages Posted: 25 Sep 2020

See all articles by Lily Morse

Lily Morse

Carnegie Mellon University - David A. Tepper School of Business

Mike Teodorescu

Boston College - Carroll School of Management; Massachusetts Institute of Technology (MIT) - D-Lab

Yazeed Awwad

D-Lab, Massachusetts Institute of Technology

Gerald C. Kane

Boston College

Date Written: September 10, 2020

Abstract

With the increase in adoption of machine learning tools by organizations risks of unfairness abound, especially when human decision processes in outcomes of socio-economic importance such as hiring, housing, lending, and admissions are automated. We reveal sources of unfair machine learning, review fairness criteria, and provide a framework which, if implemented, would enable an organization to both avoid implementing an unfair machine learning model, but also to avoid the common situation that as an algorithm learns with more data it can become unfair over time. Issues of behavioral ethics in machine learning implementations by organizations have not been thoroughly addressed in the literature, because many of the necessary concepts are dispersed across three literatures – ethics, machine learning, and management. Further, tradeoffs between fairness criteria in machine learning have not been addressed with regards to organizations. We advance the research by introducing an organizing framework for selecting and implementing fair algorithms in organizations.

Keywords: fairness, ethics, machine learning, bias, equality of opportunity, decision tree

Suggested Citation

Morse, Lily and Teodorescu, Mike and Awwad, Yazeed and Kane, Gerald C., A Framework for Fairer Machine Learning in Organizations (September 10, 2020). Available at SSRN: https://ssrn.com/abstract=3690570 or http://dx.doi.org/10.2139/ssrn.3690570

Lily Morse

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Mike Teodorescu (Contact Author)

Boston College - Carroll School of Management ( email )

140 Commonwealth Avenue
Chestnut Hill, MA 02467
United States

Massachusetts Institute of Technology (MIT) - D-Lab

265 Massachusetts Ave
N51, 3rd Floor
Cambridge, MA 02139
United States

Yazeed Awwad

D-Lab, Massachusetts Institute of Technology ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

HOME PAGE: http://d-lab.mit.edu

Gerald C. Kane

Boston College ( email )

140 Commonwealth Avenue
Chestnut Hill, MA 02467
United States

HOME PAGE: http://www.profkane.com

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