Fairness Analysis in AI Algorithms in Healthcare: A Study on Post-Processing Approaches

Abstract

Equity in Artificial Intelligence (AI) algorithms applied to healthcare is an ever-evolving field of study with significant implications for the quality and fairness of healthcare. This work focuses on applying data analysis to investigate biases in a healthcare dataset and examining how different post-processing techniques, which are less utilized and discussed in the literature compared to pre-processing techniques, can be employed to address these biases. We analyzed the Stroke Prediction dataset, and bias was identified and analyzed along with its correlation with the data. Subsequently, post-processing techniques were applied to reduce these biases, and the effectiveness of these techniques was analyzed. It was found that while all adopted post-processing techniques reduced biases, this came at the cost of a decrease in classification accuracy and precision. Among them, the EqOddsPostprocessing technique from the AIF360 library demonstrated the least impact on model accuracy and precision.

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