mlsanityData to 7 Oct 2026

fairret: a Framework for Differentiable Fairness Regularization Terms

arXiv 2310.17256 ↗First posted 26 Oct 2023cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

Current fairness toolkits in machine learning only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines. We introduce a framework of fairness regularization terms (fairrets) which quantify bias as modular, flexible objectives that are easily integrated in automatic differentiation pipelines. By employing a general definition of fairness in terms of linear-fractional statistics, a wide class of fairrets can be computed efficiently. Experiments show the behavior of their gradients and their utility in enforcing fairness with minimal loss of predictive power compared to baselines. Our contribution includes a PyTorch implementation of the fairret framework.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 26 Oct 2023.

Builds on

27 of 54 references.

References · 27

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2021Convex optimizationcited by 2,479
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  5. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
  8. 2018A Reductions Approach to Fair Classificationcited by 585
  9. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  10. 2017Algorithmic decision making and the cost of fairnesscited by 402
  11. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  12. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
15 more
  1. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  2. 2017A Rewriting System for Convex Optimization Problemscited by 178
  3. 2017A Convex Framework for Fair Regressioncited by 147
  4. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  5. 2021Fair Mixup: Fairness via Interpolationcited by 109
  6. 2017Regularization for Deep Learning: A Taxonomycited by 91
  7. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  8. 2022Algorithmic Fairness Datasets: the Story so Farcited by 51
  9. 2018FNNC: Achieving Fairness through Neural Networkscited by 43
  10. 2017Fair Kernel Learningcited by 39
  11. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  12. 2020Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithmcited by 23
  13. 2021The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizercited by 17
  14. 2022Optimal Transport of Classifiers to Fairnesscited by 12
  15. 2022Inherent Limitations of AI Fairnesscited by 12

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 5.

7 papers.

  1. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  2. 2024ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methodscited by 8
  3. 2025OxEnsemble: Fair Ensembles for Low-Data Classificationcited by 1
  4. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  5. 2025A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparitiescited by 0
  6. 2025Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networkscited by 0
  7. 2025humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problemscited by 0