mlsanityData to 7 Oct 2026

Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking

arXiv 2302.08018 ↗First posted 16 Feb 2023cs.SE cs.AIcited by 14

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Abstract (arXiv)

The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern. Addressing these fairness bugs often involves sacrificing ML performance, such as accuracy. To address this issue, we present a novel counterfactual approach that uses counterfactual thinking to tackle the root causes of bias in ML software. In addition, our approach combines models optimized for both performance and fairness, resulting in an optimal solution in both aspects. We conducted a thorough evaluation of our approach on 10 benchmark tasks using a combination of 5 performance metrics, 3 fairness metrics, and 15 measurement scenarios, all applied to 8 real-world datasets. The conducted extensive evaluations show that the proposed method significantly improves the fairness of ML software while maintaining competitive performance, outperforming state-of-the-art solutions in 84.6% of overall cases based on a recent benchmarking tool.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 33 of its 87 references that are in the map. First posted on arXiv 16 Feb 2023.

Builds on

33 of 87 references.

References · 33

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
  3. 2017Counterfactual Fairnesscited by 802
  4. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  5. 2020Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease predictioncited by 368
  6. 2017DeepXplore: Automated Whitebox Testing of Deep Learning Systemscited by 307
  7. 2017DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Carscited by 249
  8. 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 213
  9. 2020Fairness in Machine Learningcited by 194
  10. 2021A survey on datasets for fairness-aware machine learningcited by 157
  11. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  12. 2018DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systemscited by 144
21 more
  1. 2021Bias in Machine Learning Software: Why? How? What to do?cited by 92
  2. 2022Natural Attack for Pre-trained Models of Codecited by 72
  3. 2019FAHT: An Adaptive Fairness-aware Decision Tree Classifiercited by 63
  4. 2022Fairness Testing: A Comprehensive Survey and Analysis of Trendscited by 61
  5. 2020Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairnesscited by 52
  6. 2021Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipelinecited by 49
  7. 2021NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identificationcited by 42
  8. 2017Oversampling for Imbalanced Learning Based on K-Means and SMOTEcited by 38
  9. 2021FARF: A Fair and Adaptive Random Forests Classifiercited by 37
  10. 2019Automatic Testing and Improvement of Machine Translationcited by 35
  11. 2022Longitudinal Fairness with Censorshipcited by 21
  12. 2021BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systemscited by 20
  13. 2022FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neuronscited by 20
  14. 2022Adaptive Fairness Improvement Based on Causality Analysiscited by 17
  15. 2020Using Machine Learning to Automate Mammogram Images Analysiscited by 15
  16. 2023Fair Decision-making Under Uncertaintycited by 15
  17. 2020VerifyMed -- A blockchain platform for transparent trust in virtualized healthcare: Proof-of-conceptcited by 14
  18. 2022Revisiting Neuron Coverage Metrics and Quality of Deep Neural Networkscited by 14
  19. 2020Astraea: Grammar-based Fairness Testingcited by 11
  20. 2021BiasRV: Uncovering Biased Sentiment Predictions at Runtimecited by 10
  21. 2021CrossASR++: A Modular Differential Testing Framework for Automatic Speech Recognitioncited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 5; 2026: 1.

14 papers.

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  1. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 57
  2. 2024FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applicationscited by 15
  3. 2023FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysiscited by 12
  4. 2024Fairness Definitions in Language Models Explainedcited by 10
  5. 2025Digital Forensics in the Age of Large Language Modelscited by 10
  6. 2023Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Researchcited by 9
  7. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  8. 2025Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?cited by 4
  9. 2024A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learningcited by 3
  10. 2025AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Modelscited by 3
  11. 2025Towards Transparent AI: A Survey on Explainable Language Modelscited by 3
  12. 2024Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AIcited by 1
2 more
  1. 2024Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissionscited by 0
  2. 2026A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Modelscited by 0