mlsanityData to 14 Oct 2026

Group Fairness: Independence Revisited

arXiv 2101.02968 ↗First posted 8 Jan 2021cs.CYcited by 21

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Abstract (arXiv)

This paper critically examines arguments against independence, a measure of group fairness also known as statistical parity and as demographic parity. In recent discussions of fairness in computer science, some have maintained that independence is not a suitable measure of group fairness. This position is at least partially based on two influential papers (Dwork et al., 2012, Hardt et al., 2016) that provide arguments against independence. We revisit these arguments, and we find that the case against independence is rather weak. We also give arguments in favor of independence, showing that it plays a distinctive role in considerations of fairness. Finally, we discuss how to balance different fairness considerations.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jan 2021.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 5; 2024: 1; 2025: 7.

21 papers.

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  1. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  2. 2022Fairness Reprogrammingcited by 33
  3. 2021Gradual (In)Compatibility of Fairness Criteriacited by 8
  4. 2022Metrizing Fairnesscited by 6
  5. 2022Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Featurescited by 3
  6. 2022A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offscited by 3
  7. 2023Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systemscited by 3
  8. 2023Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequitycited by 3
  9. 2025Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspectivecited by 3
  10. 2023Insights From Insurance for Fair Machine Learningcited by 2
  11. 2024Authorship and the Politics and Ethics of LLM Watermarkscited by 2
  12. 2025Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergiescited by 2
9 more
  1. 2022Fairness and Randomness in Machine Learning: Statistical Independence and Relativizationcited by 1
  2. 2023A multidomain relational framework to guide institutional AI research and adoptioncited by 1
  3. 2023Reliability Gaps Between Groups in COMPAS Datasetcited by 1
  4. 2025Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rightscited by 1
  5. 2021A Systematic Approach to Group Fairness in Automated Decision Makingcited by 0
  6. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  7. 2025Simple and Effective Specialized Representations for Fair Classifierscited by 0
  8. 2025Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management Systemcited by 0
  9. 2025Transparent and Fair Profiling in Employment Services: Evidence from Switzerlandcited by 0