mlsanityData to 7 Oct 2026

A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set

arXiv 2003.14263 ↗First posted 31 Mar 2020stat.ML cs.CY cs.LGcited by 35

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Abstract (arXiv)

Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world. A critical question then recently arised among the population: Do algorithmic decisions convey any type of discrimination against specific groups of population or minorities? In this paper, we show the importance of understanding how a bias can be introduced into automatic decisions. We first present a mathematical framework for the fair learning problem, specifically in the binary classification setting. We then propose to quantify the presence of bias by using the standard Disparate Impact index on the real and well-known Adult income data set. Finally, we check the performance of different approaches aiming to reduce the bias in binary classification outcomes. Importantly, we show that some intuitive methods are ineffective. This sheds light on the fact trying to make fair machine learning models may be a particularly challenging task, in particular when the training observations contain a bias.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2020.

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Citing papers per year: 2021: 4; 2022: 6; 2023: 6; 2024: 5; 2025: 10; 2026: 4.

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  1. 2024Large Language Models are Geographically Biasedcited by 91
  2. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  3. 2024LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Educationcited by 28
  4. 2021Transport-based Counterfactual Modelscited by 27
  5. 2022Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methodscited by 15
  6. 2023Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societiescited by 11
  7. 2021pysentimiento: A Python Toolkit for Opinion Mining and Social NLP taskscited by 9
  8. 2021Representative & Fair Synthetic Datacited by 8
  9. 2022How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysiscited by 7
  10. 2021End-To-End Bias Mitigation: Removing Gender Bias in Deep Learningcited by 6
  11. 2025The World According to LLMs: How Geographic Origin Influences LLMs' Entity Deduction Capabilitiescited by 5
  12. 2025Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harmscited by 5
23 more
  1. 2022A Novel Regularization Approach to Fair MLcited by 3
  2. 2023Are fairness metric scores enough to assess discrimination biases in machine learning?cited by 3
  3. 2025Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tonecited by 3
  4. 2023How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?cited by 2
  5. 2025Learning with Differentially Private (Sliced) Wasserstein Gradientscited by 2
  6. 2025P2NIA: Privacy-Preserving Non-Iterative Auditingcited by 2
  7. 2025Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attackscited by 2
  8. 2025Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisationcited by 2
  9. 2022To what extent should we trust AI models when they extrapolate?cited by 1
  10. 2023Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuationscited by 1
  11. 2023When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairnesscited by 1
  12. 2024Debiasing Machine Learning Models by Using Weakly Supervised Learningcited by 1
  13. 2024Fair Graph Neural Network with Supervised Contrastive Regularizationcited by 1
  14. 2025When majority rules, minority loses: bias amplification of gradient descentcited by 1
  15. 2025Size-adaptive Hypothesis Testing for Fairnesscited by 1
  16. 2022Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variablescited by 0
  17. 2022Cluster-level Group Representativity Fairness in k-means Clusteringcited by 0
  18. 2024Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifierscited by 0
  19. 2025PLS-based approach for fair representation learningcited by 0
  20. 2026Individual Fairness In Strategic Classificationcited by 0
  21. 2026Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Networkcited by 0
  22. 2026Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scalecited by 0
  23. 2026Discovering Geometric Biases in 3D Face Reconstruction: A Curvature-Aware Spectral Framework for Fairness Evaluationcited by 0