mlsanityData to 14 Oct 2026

Why Is My Classifier Discriminatory?

arXiv 1805.12002 ↗First posted 30 May 2018stat.ML cs.LGcited by 143

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Abstract (arXiv)

Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model. We decompose cost-based metrics of discrimination into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Finally, we perform case-studies on prediction of income, mortality, and review ratings, confirming the value of this analysis. We find that data collection is often a means to reduce discrimination without sacrificing accuracy.

Key facts. Cited by 143 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 May 2018.

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Citing papers per year: 2021: 30; 2022: 29; 2023: 32; 2024: 31; 2025: 15; 2026: 6.

143 papers.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 207
  2. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 185
  3. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  4. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  5. 2022Long-Tailed Recognition via Weight Balancingcited by 107
  6. 2021Trustworthy AI: A Computational Perspectivecited by 93
  7. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 68
  8. 2021The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Modelscited by 63
  9. 2021Multi-group Agnostic PAC Learnabilitycited by 44
  10. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  11. 2022Achieving Fairness at No Utility Cost via Data Reweighing with Influencecited by 38
  12. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
38 more
  1. 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmcited by 35
  2. 2024CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?cited by 35
  3. 2021Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Researchcited by 34
  4. 2023Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classificationcited by 32
  5. 2024Most Influential Subset Selection: Challenges, Promises, and Beyondcited by 32
  6. 2021Representation Matters: Assessing the Importance of Subgroup Allocations in Training Datacited by 31
  7. 2022Repairing Neural Networks by Leaving the Right Past Behindcited by 27
  8. 2023Beyond Confidence: Reliable Models Should Also Consider Atypicalitycited by 25
  9. 2021Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Valuescited by 24
  10. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
  11. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  12. 2021Certifying Robustness to Programmable Data Bias in Decision Treescited by 21
  13. 2021Hyperparameter Optimization Is Deceiving Us, and How to Stop Itcited by 20
  14. 2022Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendationscited by 20
  15. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  16. 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
  17. 2021Understanding and Mitigating Accuracy Disparity in Regressioncited by 17
  18. 2023The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictionscited by 17
  19. 2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
  20. 2023Fairness through Aleatoric Uncertaintycited by 16
  21. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  22. 2021MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Datasetcited by 15
  23. 2022Data Representativity for Machine Learning and AI Systemscited by 15
  24. 2023Coarse race data conceals disparities in clinical risk score performancecited by 15
  25. 2024Who's in and who's out? A case study of multimodal CLIP-filtering in DataCompcited by 15
  26. 2024The Data Addition Dilemmacited by 15
  27. 2021Automatic Fairness Testing of Neural Classifiers through Adversarial Samplingcited by 14
  28. 2022Survey on Fair Reinforcement Learning: Theory and Practicecited by 14
  29. 2023Diversity and Inclusion in Artificial Intelligencecited by 14
  30. 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
  31. 2025A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMscited by 13
  32. 2021A comparison of approaches to improve worst-case predictive model performance over patient subpopulationscited by 12
  33. 2024Fairness Without Harm: An Influence-Guided Active Sampling Approachcited by 12
  34. 2022Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learningcited by 11
  35. 2024How to be fair? A study of label and selection biascited by 11
  36. 2024Rethinking Fair Graph Neural Networks from Re-balancingcited by 11
  37. 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decompositioncited by 10
  38. 2021Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimizationcited by 9

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