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Failures of Fairness in Automation Require a Deeper Understanding of Human–ML Augmentation

Mike H. M. Teodorescu1; Lily Morse2; Yazeed Awwad3; Gerald C. Kane1

1 Carroll School of Management, Boston College, Fulton 460, 140 Commonwealth Avenue, Chestnut Hill, MA 02467 U.S.A. · 2 John Chambers College of Business and Economics, West Virginia University, Morgantown, WV 26506 U.S.A. · 3 Center for Complex Systems, King Abdulaziz City for Science & Technology, Riyadh 12354 SAUDI ARABIA, and Massachusetts Institute of Technology, 77 Massachusetts Avenue, Building E18-309, Cambridge, MA 02139 U.S.A.

MIS Quarterly 2021

Machine learning (ML) tools reduce the costs of performing repetitive, time-consuming tasks yet run the risk of introducing systematic unfairness into organizational processes. Automated approaches to achieving fairness often fail in complex situations, leading some researchers to suggest that human augmentation of ML tools is necessary. However, our current understanding of human–ML augmentation remains limited. In this paper, we argue that the Information Systems (IS) discipline needs a more sophisticated view of and research into human–ML augmentation. We introduce a typology of augmentation for fairness consisting of four quadrants: reactive oversight, proactive oversight, informed reliance, and supervised reliance. We identify significant intersections with previous IS research and distinct managerial approaches to fairness for each quadrant. Several potential research questions emerge from fundamental differences between ML tools trained on data and traditional IS built with code. IS researchers may discover that the differences of ML tools undermine some of the fundamental assumptions upon which classic IS theories and concepts rest. ML may require massive rethinking of significant portions of the corpus of IS research in light of these differences, representing an exciting frontier for research into human–ML augmentation in the years ahead that IS researchers should embrace. 1

DOI
10.25300/misq/2021/16535
Volume
45 (3)
Pages
1483-1500
Language
en
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