mlsanityData to 14 Oct 2026

Standardized Interpretable Fairness Measures for Continuous Risk Scores

arXiv 2308.11375 ↗First posted 22 Aug 2023stat.ML cs.AI cs.LGcited by 3

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Abstract (arXiv)

We propose a standardized version of fairness measures for continuous scores with a reasonable interpretation based on the Wasserstein distance. Our measures are easily computable and well suited for quantifying and interpreting the strength of group disparities as well as for comparing biases across different models, datasets, or time points. We derive a link between the different families of existing fairness measures for scores and show that the proposed standardized fairness measures outperform ROC-based fairness measures because they are more explicit and can quantify significant biases that ROC-based fairness measures miss.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 46 references that are in the map. First posted on arXiv 22 Aug 2023.

Builds on

25 of 46 references.

References · 25

  1. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  2. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  3. 2021Tabular Data: Deep Learning is Not All You Needcited by 535
  4. 2017On Fairness and Calibrationcited by 406
  5. 2019Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classificationcited by 404
  6. 2017Algorithmic decision making and the cost of fairnesscited by 403
  7. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 369
  8. 2019Verified Uncertainty Calibrationcited by 309
  9. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  10. 2019Wasserstein Fair Classificationcited by 131
  11. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  12. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
13 more
  1. 2020Fair Regression with Wasserstein Barycenterscited by 100
  2. 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
  3. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  4. 2020Survey on Causal-based Machine Learning Fairness Notionscited by 54
  5. 2019The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metriccited by 41
  6. 2022Causal Conceptions of Fairness and their Consequencescited by 38
  7. 2022Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecited by 16
  8. 2020Wasserstein-based fairness interpretability framework for machine learning modelscited by 14
  9. 2020The Impossibility Theorem of Machine Fairness -- A Causal Perspectivecited by 13
  10. 2021Costs and Benefits of Fair Regressioncited by 8
  11. 2023Mean Parity Fair Regression in RKHScited by 6
  12. 2021Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metricscited by 5
  13. 2021Everything is Relative: Understanding Fairness with Optimal Transportcited by 2

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment frameworkcited by 3
  2. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  3. 2025Explainable post-training bias mitigation with distribution-based fairness metricscited by 0