mlsanityData to 13 Oct 2026

Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency

arXiv 2206.02237 ↗First posted 5 Jun 2022cs.CY cs.LGcited by 19

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Abstract (arXiv)

Binary decision making classifiers are not fair by default. Fairness requirements are an additional element to the decision making rationale, which is typically driven by maximizing some utility function. In that sense, algorithmic fairness can be formulated as a constrained optimization problem. This paper contributes to the discussion on how to implement fairness, focusing on the fairness concepts of positive predictive value (PPV) parity, false omission rate (FOR) parity, and sufficiency (which combines the former two). We show that group-specific threshold rules are optimal for PPV parity and FOR parity, similar to well-known results for other group fairness criteria. However, depending on the underlying population distributions and the utility function, we find that sometimes an upper-bound threshold rule for one group is optimal: utility maximization under PPV parity (or FOR parity) might thus lead to selecting the individuals with the smallest utility for one group, instead of selecting the most promising individuals. This result is counter-intuitive and in contrast to the analogous solutions for statistical parity and equality of opportunity. We also provide a solution for the optimal decision rules satisfying the fairness constraint sufficiency. We show that more complex decision rules are required and that this leads to within-group unfairness for all but one of the groups. We illustrate our findings based on simulated and real data.

Key facts. Cited by 19 papers in the map (data through 13 Oct 2026). Builds on 18 of its 53 references that are in the map. First posted on arXiv 5 Jun 2022.

Builds on

18 of 53 references.

References · 18

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,005
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,915
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  5. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 585
  6. 2017On Fairness and Calibrationcited by 406
  7. 2017Algorithmic decision making and the cost of fairnesscited by 402
  8. 2020Fairness in Machine Learning: A Surveycited by 370
  9. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  10. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  11. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  12. 2020Algorithmic Fairnesscited by 143
6 more
  1. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  2. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  3. 2020Fairness Metrics: A Comparative Analysiscited by 45
  4. 2018From Soft Classifiers to Hard Decisions: How fair can we be?cited by 16
  5. 2018Enhancing the Accuracy and Fairness of Human Decision Makingcited by 13
  6. 2021Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?cited by 10

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 3; 2025: 3; 2026: 2.

19 papers.

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  1. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  2. 2023Consensus and Subjectivity of Skin Tone Annotation for ML Fairnesscited by 22
  3. 2023To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methodscited by 19
  4. 2023A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systemscited by 12
  5. 2023On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Modelcited by 6
  6. 2023Deceptive Fairness Attacks on Graphs via Meta Learningcited by 6
  7. 2023Fairness and Sequential Decision Making: Limits, Lessons, and Opportunitiescited by 5
  8. 2023Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairnesscited by 4
  9. 2022A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offscited by 3
  10. 2022Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metricscited by 3
  11. 2024Cherry on the Cake: Fairness is NOT an Optimization Problemcited by 2
  12. 2022Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementationcited by 1
7 more
  1. 2025Size-adaptive Hypothesis Testing for Fairnesscited by 1
  2. 2026Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systemscited by 1
  3. 2024Assessing Group Fairness with Social Welfare Optimizationcited by 0
  4. 2024A Semidefinite Relaxation Approach for Fair Graph Clusteringcited by 0
  5. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  6. 2025Fair Classification by Direct Intervention on Operating Characteristicscited by 0
  7. 2026Fair Decisions from Calibrated Scores: Achieving Optimal Classification While Satisfying Sufficiencycited by 0