Discrimination in machine learning algorithms
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Abstract (arXiv)
Machine learning algorithms are routinely used for business decisions that may directly affect individuals, for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (like sex or race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 10 references that are in the map. First posted on arXiv 30 Jun 2022.
Builds on
2 of 10 references.
References · 2
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2014Certifying and removing disparate impactcited by 715
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