Fair Classification by Direct Intervention on Operating Characteristics
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Abstract (arXiv)
We develop new classifiers under group fairness in the attribute-aware setting for binary classification with multiple group fairness constraints (e.g., demographic parity (DP), equalized odds (EO), and predictive parity (PP)). We propose a novel approach, applicable to linear fractional constraints, based on directly intervening on the operating characteristics of a pre-trained base classifier, by (i) identifying optimal operating characteristics using the base classifier's group-wise ROC convex hulls and (ii) post-processing the base classifier to match those targets. As practical post-processors, we consider randomizing a mixture of group-wise thresholding rules subject to minimizing the expected number of interventions. We further extend our approach to handle multiple protected attributes and multiple linear fractional constraints. On standard datasets (COMPAS and ACSIncome), our methods simultaneously satisfy approximate DP, EO, and PP with few interventions and a near-oracle drop in accuracy; comparing favorably to previous methods.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 51 references that are in the map. First posted on arXiv 29 Sep 2025.
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References · 29
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
- 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
- 2018A Reductions Approach to Fair Classificationcited by 587
- 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 449
- 2017On Fairness and Calibrationcited by 406
- 2017Algorithmic decision making and the cost of fairnesscited by 403
- 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 174
- 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
- 2023Fairlearn: Assessing and Improving Fairness of AI Systemscited by 74
- 2019On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learningcited by 49
17 more
- 2024Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologiescited by 47
- 2020Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasingcited by 32
- 2022Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiencycited by 19
- 2023The Possibility of Fairness: Revisiting the Impossibility Theorem in Practicecited by 18
- 2024Auditing Work: Exploring the New York City algorithmic bias audit regimecited by 18
- 2024When is Multicalibration Post-Processing Necessary?cited by 18
- 2021fairadapt: Causal Reasoning for Fair Data Pre-processingcited by 16
- 2022Pushing the limits of fairness impossibility: Who's the fairest of them all?cited by 15
- 2021Fair Enough: Searching for Sufficient Measures of Fairnesscited by 14
- 2024Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processingcited by 10
- 2024A Unified Post-Processing Framework for Group Fairness in Classificationcited by 7
- 2025Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspectivecited by 5
- 2023Causal Fair Machine Learning via Rank-Preserving Interventional Distributionscited by 4
- 2022Beyond Incompatibility: Trade-offs between Mutually Exclusive Fairness Criteria in Machine Learning and Lawcited by 3
- 2024A Unified View of Group Fairness Tradeoffs Using Partial Information Decompositioncited by 3
- 2023Fair Enough? A map of the current limitations of the requirements to have fair algorithmscited by 2
- 2024FROC: Building Fair ROC from a Trained Classifiercited by 1
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