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Fairness in Machine Learning: A Survey
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As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.
Forward citations
Cited by 6 Pith papers
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Exploring Fairness Interventions in Open Source Projects
Only 32% of 62 open source fairness interventions are actively maintained, and support is concentrated in classification models and inprocessing mitigation.
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Fairness and Efficiency in Human-Agent Teams: An Iterative Algorithm Design Approach
A new fairness metric and algorithm, FEA, are proposed and tested in simulation and two small user studies, with weak evidence that FEA improves perceived fairness.
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Exploring the Landscape of Fairness Interventions in Software Engineering
A survey of fairness interventions in software engineering that organizes prior work into a taxonomy and adds a small empirical analysis of open-source fairness repository maintenance.
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