Mitigating Bias in Medical Datasets: A Comparative Analysis of Generative Adversarial Networks (GANs) Based Data Generation Techniques

Hameed, Mohamed Ashik Shahul and Qureshi, Asifa Mehmood and Kaushik, Abishek (2024) Mitigating Bias in Medical Datasets: A Comparative Analysis of Generative Adversarial Networks (GANs) Based Data Generation Techniques. In: Proceedings of The 32nd Irish Conference on Artificial Intelligence and Cognitive Science, December 9-10, 2024, Dublin, Republic of Ireland.

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Abstract

The increasing use of Artificial intelligence (AI) in the medical domain has highlighted a critical issue: bias in datasets. Biases in medical datasets can lead to skewed predictions, unfair clinical decisions, incorrect diagnoses and poor generalisation of AI models. Very often, these biases are the consequence of imbalance in the dataset. Generative Adversarial Networks (GANs) have appeared to be a promising solution for solving the data imbalance issue. Synthetic data can help mitigate bias by balancing the dataset for sensitive attributes as well as for class labels. However, the efficiency of different GAN variants in mitigating bias remains unexplored in the medical domain. This paper investigates and compares various GAN variants to identify the most effective approach to producing balanced data. In this study, we evaluated different variants of GAN on three medical datasets with the aim of contributing to the development of more fairer and inclusive AI models in the medical domain. The study shows that the performance of the Machine Learning (ML) model improves when the dataset is balanced using synthetic data samples. Moreover, the MedGAN variant performs better when compared with other variants of GAN.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Bias; Fairness; Medical datasets; GANs; TGAN; CTGAN; MedGAN; MC-MedGAN.
Subjects:	Computer Science
Research Centres:	Regulated Software Research Centre
Depositing User:	Sean McGreal
Date Deposited:	17 Dec 2025 10:04
Last Modified:	17 Dec 2025 10:04
License:	Creative Commons: Attribution-Noncommercial-Share Alike 4.0
URI:	https://eprints.dkit.ie/id/eprint/986

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