Generating Fair Universal Representations using Adversarial Models

Kairouz, Peter; Liao, Jiachun; Huang, Chong; Vyas, Maunil R.; Monica, Welfert,; Sankar, Lalitha

doi:10.48550/arxiv.1910.00411

2019

DOI: 10.48550/arxiv.1910.00411

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Generating Fair Universal Representations using Adversarial Models

Peter Kairouz¹,

Jiachun Liao²,

Chong Huang³

et al.

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2024

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Fairness in Machine Learning: A Survey

Caton,

Haas

2024

ACM Comput. Surv.

108

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When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any 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 that aim to increase the fairness of Machine Learning. 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, and unsupervised learning is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as five dilemmas for fairness research.

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Fairness in Machine Learning: A Survey

Caton,

Haas

2024

ACM Comput. Surv.

108

View full text Add to dashboard Cite

show abstract

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Generating Fair Universal Representations using Adversarial Models

Cited by 1 publication

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Fairness in Machine Learning: A Survey

Fairness in Machine Learning: A Survey

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