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Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 3/2023

24.02.2022 | Research Paper

Towards fair machine learning using combinatorial methods

verfasst von: Anant Saraswat, Manjish Pal, Subham Pokhriyal, Kumar Abhishek

Erschienen in: Evolutionary Intelligence | Ausgabe 3/2023

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Abstract

With the rise of artificial intelligence and machine learning in the last decade, there has been an increasing interest in developing a solid theory and implementing algorithmic fairness, which has eventually resulted in a large volume of work over the past few years. Despite the enormous amount of work done on the topic over a concise period, there has been little consensus of a unifying theory of algorithmic fairness. In this paper, we develop a notion of fairness that is based on the notion of discrepancy of set systems, a widely studied topic in the theory of computer science and combinatorics. (Chazelle Bernard in The discrepancy method: randomness and complexity. Cambridge University Press (2001)).
Vorheriger Artikel Evolutionary approaches for the weighted anti-covering location problem
Nächster Artikel Grey wolf optimization algorithm based on dynamically adjusting inertial weight and levy flight strategy

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Metadaten
Titel
Towards fair machine learning using combinatorial methods
verfasst von
Anant Saraswat
Manjish Pal
Subham Pokhriyal
Kumar Abhishek
Publikationsdatum
24.02.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Evolutionary Intelligence / Ausgabe 3/2023
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-022-00702-5

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