Abstract:
This research explores the evolving landscape of Explainable Artificial Intelligence (XAI), with a specific focus on counterfactual explanations and their role in ensuring fairness and reducing bias in AI decision-making. As AI systems become increasingly integrated into critical sectors like healthcare and finance, the need for transparent, understandable, and fair AI is paramount. To address the gaps, we propose a novel framework that uses a Flexibly Fair Variational Autoencoder (FFVAE) and Counterfactual Regression Network (CFRnet). This approach aims to segregate sensitive attributes into distinct latent spaces, enabling the generation of fair and unbiased counterfactual predictions.
