mlsanityData to 14 Oct 2026

Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning

arXiv 2505.05471 ↗First posted 8 May 2025cs.CY cs.LGcited by 0

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Abstract (arXiv)

Leveraging current legal standards, we define bias through the lens of marginal benefits and objective testing with the novel metric "Objective Fairness Index". This index combines the contextual nuances of objective testing with metric stability, providing a legally consistent and reliable measure. Utilizing the Objective Fairness Index, we provide fresh insights into sensitive machine learning applications, such as COMPAS (recidivism prediction), highlighting the metric's practical and theoretical significance. The Objective Fairness Index allows one to differentiate between discriminatory tests and systemic disparities.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 8 May 2025.

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11 of 28 references.

References · 11

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2020Fairness in Machine Learning: A Surveycited by 370
  3. 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 218
  4. 2022A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Predictioncited by 18
  5. 2022Debiasing Learning for Membership Inference Attacks Against Recommender Systemscited by 12
  6. 2023Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobaocited by 11
  7. 2022Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)cited by 6
  8. 2023Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformerscited by 3
  9. 2023Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systemscited by 3
  10. 2023Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness settingcited by 2
  11. 2023Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approachcited by 1

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