mlsanityData to 14 Oct 2026

OxonFair: A Flexible Toolkit for Algorithmic Fairness

arXiv 2407.13710 ↗First posted 30 Jun 2024cs.CY cs.AI cs.LGcited by 12

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Abstract (arXiv)

We present OxonFair, a new open source toolkit for enforcing fairness in binary classification. Compared to existing toolkits: (i) We support NLP and Computer Vision classification as well as standard tabular problems. (ii) We support enforcing fairness on validation data, making us robust to a wide range of overfitting challenges. (iii) Our approach can optimize any measure based on True Positives, False Positive, False Negatives, and True Negatives. This makes it easily extensible and much more expressive than existing toolkits. It supports all 9 and all 10 of the decision-based group metrics of two popular review articles. (iv) We jointly optimize a performance objective alongside fairness constraints. This minimizes degradation while enforcing fairness, and even improves the performance of inadequately tuned unfair baselines. OxonFair is compatible with standard ML toolkits, including sklearn, Autogluon, and PyTorch and is available at https://github.com/oxfordinternetinstitute/oxonfair

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 68 of its 106 references that are in the map. First posted on arXiv 30 Jun 2024.

Builds on

68 of 106 references.

References · 68

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38 more
  1. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 449
  2. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 434
  3. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  4. 2017On Fairness and Calibrationcited by 406
  5. 2017Algorithmic decision making and the cost of fairnesscited by 403
  6. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 384
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  17. 2018Path-Specific Counterfactual Fairnesscited by 181
  18. 2019REPAIR: Removing Representation Bias by Dataset Resamplingcited by 157
  19. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 153
  20. 2019Queens are Powerful too: Mitigating Gender Bias in Dialogue Generationcited by 152
  21. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  22. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 147
  23. 2020Fair Attribute Classification through Latent Space De-biasingcited by 125
  24. 2018Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddingscited by 114
  25. 2021Fair Mixup: Fairness via Interpolationcited by 109
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  27. 2021An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Modelscited by 97
  28. 2021Bias in Machine Learning Software: Why? How? What to do?cited by 93
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  31. 2020Fairness by Learning Orthogonal Disentangled Representationscited by 76
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  35. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
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50 most cited of 68 All 68 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 4.

12 papers.

  1. 2025Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphscited by 2
  2. 2025Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Toolcited by 1
  3. 2025ECHO: A Participatory Framework for Bias-Anchored AI Harm Anticipationcited by 1
  4. 2025OxEnsemble: Fair Ensembles for Low-Data Classificationcited by 1
  5. 2026Benchmarking Bias Mitigation Toward Fairness Without Harm from Vision to LVLMscited by 1
  6. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  7. 2025EquiPy: Sequential Fairness using Optimal Transport in Pythoncited by 0
  8. 2025Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networkscited by 0
  9. 2025Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learningcited by 0
  10. 2026Fair Decisions from Calibrated Scores: Achieving Optimal Classification While Satisfying Sufficiencycited by 0
  11. 2026Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effectscited by 0
  12. 2026Adaptive Calibration for Fair and Performant Facial Recognitioncited by 0