Do the ends justify the means?: variation in the distributive and procedural fairness of machine learning algorithms
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 cr...
| Authors: | ; ; ; |
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| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
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| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
Springer Science + Business Media B. V
2022
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| In: |
Journal of business ethics
Year: 2022, Volume: 181, Issue: 4, Pages: 1083-1095 |
| Further subjects: | B
Distributive fairness
B Procedural Fairness B Fair play B Aufsatz in Zeitschrift B Algorithm design B machine learning |
| Online Access: |
Presumably Free Access Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
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| 245 | 1 | 0 | |a Do the ends justify the means? |b variation in the distributive and procedural fairness of machine learning algorithms |c Lily Morse, Mike Horia M. Teodorescu, Yazeed Awwad, Gerald C. Kane |
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| 520 | |a 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. | ||
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