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Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

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Related Experiment Video

Updated: Sep 13, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare.

Vien Ngoc Dang¹, Víctor M Campello¹, Jerónimo Hernández-González²

¹Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.

Journal of Healthcare Informatics Research

|July 29, 2025

Summary

Post-processing methods mitigate bias in machine learning healthcare models without retraining. This study evaluates state-of-the-art debiasing techniques, revealing trade-offs between fairness and accuracy for optimal implementation.

Keywords:

Algorithmic bias Fairness Healthcare Machine learning classifiers Post-processing

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Area of Science:

Machine Learning
Healthcare Disparities
Algorithmic Fairness

Background:

Machine learning classifiers in healthcare can perpetuate or worsen existing health disparities due to biased training data.
Addressing these biases is crucial for equitable healthcare delivery.

Purpose of the Study:

To rigorously compare state-of-the-art post-processing debiasing methods for machine learning models in healthcare.
To evaluate the trade-offs between predictive performance and various group fairness metrics.

Main Methods:

Comparison of multiple post-processing debiasing techniques.
Evaluation across diverse synthetic and real-world healthcare datasets.
Assessment using various performance and fairness metrics, including impact on untreated attributes.

Main Results:

Identification of strengths and weaknesses for each compared debiasing method.
Analysis of the trade-offs between group fairness and predictive performance.
Examination of trade-offs among different notions of group fairness and impact on untreated attributes.

Conclusions:

Post-processing methods offer a privacy-preserving way to ensure fairness without retraining.
Optimal implementation in healthcare requires balancing accuracy and fairness based on specific needs.
The study provides insights for effectively mitigating bias in healthcare machine learning.