Do the Ends Justify the Means? Variation in the Distributive and Procedural Fairness of Machine Learning Algorithms
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10551_2021_4939_ReferencePDF.pdf
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Author(s)
Morse, Lily
Teodorescu, Mike H. M.
Awwad, Yazeed
Kane, Gerald C.
Date Issued
October 18, 2021
Publisher
Springer Netherlands
Citation
Morse, Lily, Teodorescu, Mike H. M., Awwad, Yazeed and Kane, Gerald C. 2021. "Do the Ends Justify the Means? Variation in the Distributive and Procedural Fairness of Machine Learning Algorithms."
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Author's final manuscript
Abstract
Abstract
Recent advances in machine learning methods have created opportunities to eliminate unfairness from algorithmic decision making. Multiple computational techniques (i.e., algorithmic fairness criteria) have arisen out of this work. Yet, urgent questions remain about the perceived fairness of these criteria and in which situations organizations should use them. In this paper, we seek to gain insight into these questions by exploring fairness perceptions of five algorithmic criteria. We focus on two key dimensions of fairness evaluations: distributive fairness and procedural fairness. We shed light on variation in the potential for different algorithmic criteria to facilitate distributive fairness. Subsequently, we discuss procedural fairness and provide a framework for understanding how algorithmic criteria relate to essential aspects of this construct, which helps to identify when a specific criterion is suitable. From a practical standpoint, we encourage organizations to recognize that managing fairness in machine learning systems is complex, and that adopting a blind or one-size-fits-all mentality toward algorithmic criteria will surely damage people’s attitudes and trust in automated technology. Instead, firms should carefully consider the subtle yet significant differences between these technical solutions.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s10551-021-04939-5
