2019
DOI: 10.1007/s10618-019-00658-8
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Matching code and law: achieving algorithmic fairness with optimal transport

Abstract: Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFAθ ) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary bet… Show more

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Cited by 23 publications
(9 citation statements)
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References 38 publications
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“…In fact, RSs are known to be multi-stakeholder environments (Abdollahpouri et al 2019a), since they affect multiple actors in a direct way, mainly the users receiving the recommendations (consumers) and those behind the recommended objects (providers). Because of that, research on bias analysis and fairness measurements is needed; in particular, specific definitions, dependency variables, and mitigation approaches beyond those already studied for general Machine Learning (Zehlike et al 2020).…”
Section: Introductionmentioning
confidence: 99%
“…In fact, RSs are known to be multi-stakeholder environments (Abdollahpouri et al 2019a), since they affect multiple actors in a direct way, mainly the users receiving the recommendations (consumers) and those behind the recommended objects (providers). Because of that, research on bias analysis and fairness measurements is needed; in particular, specific definitions, dependency variables, and mitigation approaches beyond those already studied for general Machine Learning (Zehlike et al 2020).…”
Section: Introductionmentioning
confidence: 99%
“…The algorithm referred to as FLIP is a popular fair link prediction algorithm performing graph structural debias by reducing homophily present in the graph and measures fairness using reduction in modularity. Zehlike et al 40 . It is a optimal transport based score transformation algorithm to tackle intersectional attributes.…”
Section: Baselinesmentioning
confidence: 99%
“…In this paper, we apply the score transformation approach to graph data by taking the former route and using quadratic programming to transform the scores to fair ones. We also use the algorithm of Zehlike 40 , that follows the other approach, as a baseline, by suitably adapting it to the graph data.…”
Section: Score Transformationmentioning
confidence: 99%
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