mlsanityData to 7 Oct 2026

The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness

arXiv 2202.09519 ↗First posted 19 Feb 2022cs.CY cs.AI cs.LGcited by 21

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Abstract (arXiv)

Computer scientists are trained to create abstractions that simplify and generalize. However, a premature abstraction that omits crucial contextual details creates the risk of epistemic trespassing, by falsely asserting its relevance into other contexts. We study how the field of responsible AI has created an imperfect synecdoche by abstracting the four-fifths rule (a.k.a. the 4/5 rule or 80% rule), a single part of disparate impact discrimination law, into the disparate impact metric. This metric incorrectly introduces a new deontic nuance and new potentials for ethical harms that were absent in the original 4/5 rule. We also survey how the field has amplified the potential for harm in codifying the 4/5 rule into popular AI fairness software toolkits. The harmful erasure of legal nuances is a wake-up call for computer scientists to self-critically re-evaluate the abstractions they create and use, particularly in the interdisciplinary field of AI ethics.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 12 of its 87 references that are in the map. First posted on arXiv 19 Feb 2022.

Builds on

12 of 87 references.

References · 12

  1. 2018Datasheets for Datasetscited by 1,371
  2. 2018Model Cards for Model Reportingcited by 1,068
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2019Measurement and Fairnesscited by 299
  5. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  6. 2018Categorizing Variants of Goodhart's Lawcited by 132
  7. 2021It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarkscited by 61
  8. 2019What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systemscited by 38
  9. 2022Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethicscited by 28
  10. 2019On the Legal Compatibility of Fairness Definitionscited by 19
  11. 2020Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Contextcited by 18
  12. 2019Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Sciencecited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 5; 2025: 6; 2026: 2.

21 papers.

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  1. 2023Fairlearn: Assessing and Improving Fairness of AI Systemscited by 74
  2. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  3. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  4. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  5. 2023Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Treecited by 14
  6. 2023Scaling Laws Do Not Scalecited by 13
  7. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  8. 2025Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitmentcited by 12
  9. 2024Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithmscited by 5
  10. 2024Towards Standardizing AI Bias Explorationcited by 5
  11. 2025Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learningcited by 5
  12. 2023Fairness Vs. Personalization: Towards Equity in Epistemic Utilitycited by 2
9 more
  1. 2024Formalising Anti-Discrimination Law in Automated Decision Systemscited by 2
  2. 2025Critical Appraisal of Fairness Metrics in Clinical Predictive AIcited by 2
  3. 2022Lost in Translation: Reimagining the Machine Learning Life Cycle in Educationcited by 1
  4. 2025A multimodal Bayesian Network for symptom-level depression and anxiety prediction from voice and speech datacited by 1
  5. 2024Evaluating AI Group Fairness: a Fuzzy Logic Perspectivecited by 0
  6. 2025FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methodscited by 0
  7. 2025seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairnesscited by 0
  8. 2026Optimal Recourse Summaries via Bi-Objective Decision Tree Learningcited by 0
  9. 2026VFR-Audit: Verdict-Level Reliability for Fairness Audits in Hospital Length-of-Stay Predictioncited by 0