mlsanityData to 14 Oct 2026

Fairness in Machine Learning meets with Equity in Healthcare

arXiv 2305.07041 ↗First posted 11 May 2023cs.LGcited by 5

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Abstract (arXiv)

With the growing utilization of machine learning in healthcare, there is increasing potential to enhance healthcare outcomes. However, this also brings the risk of perpetuating biases in data and model design that can harm certain demographic groups based on factors such as age, gender, and race. This study proposes an artificial intelligence framework, grounded in software engineering principles, for identifying and mitigating biases in data and models while ensuring fairness in healthcare settings. A case study is presented to demonstrate how systematic biases in data can lead to amplified biases in model predictions, and machine learning methods are suggested to prevent such biases. Future research aims to test and validate the proposed ML framework in real-world clinical settings to evaluate its impact on promoting health equity.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 46 references that are in the map. First posted on arXiv 11 May 2023.

Builds on

11 of 46 references.

References · 11

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2017Counterfactual Fairnesscited by 804
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  5. 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 439
  6. 2017On Fairness and Calibrationcited by 406
  7. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  8. 2018Fair Clustering Through Fairletscited by 176
  9. 2016Understanding Deep Convolutional Networkscited by 76
  10. 2022Dbias: Detecting biases and ensuring Fairness in news articlescited by 28
  11. 2022A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmissioncited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.

  1. 2024FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?cited by 9
  2. 2024Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Reviewcited by 6
  3. 2023Navigating News Narratives: A Media Bias Analysis Datasetcited by 1
  4. 2026From Features to Actions: Explainability in Traditional and Agentic AI Systemscited by 1
  5. 2024Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissionscited by 0