Fairness definitions explained

Algorithm fairness has started to attract the attention of researchers in AI, Software Engineering and Law communities, with more than twenty different notions of fairness proposed in the last few years. Yet, there is no clear agreement on which definition to apply in each situation. Moreover, the d...

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Bibliographic Details
Published in:2018 IEEE ACM International Workshop on Software Fairness (FairWare) pp. 1 - 7
Main Authors: Verma, Sahil, Rubin, Julia
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 29.05.2018
IEEE/ACM
Series:ACM Conferences
Subjects:
Artificial intelligence
Conferences
definitions of fairness
Employment
History
machine learning
Software
Software algorithms
Software engineering
survey
ISBN:9781450357463, 1450357466
Online Access:Get full text
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Summary:Algorithm fairness has started to attract the attention of researchers in AI, Software Engineering and Law communities, with more than twenty different notions of fairness proposed in the last few years. Yet, there is no clear agreement on which definition to apply in each situation. Moreover, the detailed differences between multiple definitions are difficult to grasp. To address this issue, this paper collects the most prominent definitions of fairness for the algorithmic classification problem, explains the rationale behind these definitions, and demonstrates each of them on a single unifying case-study. Our analysis intuitively explains why the same case can be considered fair according to some definitions and unfair according to others.
ISBN:9781450357463
1450357466
DOI:10.1145/3194770.3194776