mlsanityData to 7 Oct 2026

Review of Mathematical frameworks for Fairness in Machine Learning

arXiv 2005.13755 ↗First posted 26 May 2020stat.ML cs.LGcited by 21

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Abstract (arXiv)

A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented from a mathematical point of view. Following our independence-based approach, we consider how to build fair algorithms and the consequences on the degradation of their performance compared to the possibly unfair case. This corresponds to the price for fairness given by the criteria textitstatistical parity or textitequality of odds. Novel results giving the expressions of the optimal fair classifier and the optimal fair predictor (under a linear regression gaussian model) in the sense of textitequality of odds are presented.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 3; 2023: 5; 2024: 2; 2025: 4; 2026: 1.

21 papers.

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  1. 2022Improving the Fairness of Chest X-ray Classifierscited by 52
  2. 2021Classification with abstention but without disparitiescited by 27
  3. 2021Achieving Fairness with a Simple Ridge Penaltycited by 13
  4. 2021Fairness with Continuous Optimal Transportcited by 9
  5. 2025Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Modelcited by 5
  6. 2023Fairness Uncertainty Quantification: How certain are you that the model is fair?cited by 4
  7. 2023fairml: A Statistician's Take on Fair Machine Learning Modellingcited by 4
  8. 2021Quantum Fair Machine Learningcited by 3
  9. 2021Subgroup Fairness in Two-Sided Marketscited by 3
  10. 2022A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysiscited by 3
  11. 2022Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributescited by 3
  12. 2025Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tonecited by 3
9 more
  1. 2024Demographic parity in regression and classification within the unawareness frameworkcited by 2
  2. 2023When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairnesscited by 1
  3. 2023Measuring and Mitigating Biases in Motor Insurance Pricingcited by 1
  4. 2024Debiasing Machine Learning Models by Using Weakly Supervised Learningcited by 1
  5. 2025When majority rules, minority loses: bias amplification of gradient descentcited by 1
  6. 2021Computability, Complexity, Consistency and Controllability: A Four C's Framework for cross-disciplinary Ethical Algorithm Researchcited by 0
  7. 2023Assessing Perceived Fairness from Machine Learning Developer's Perspectivecited by 0
  8. 2025Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Informationcited by 0
  9. 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Datacited by 0