Achieving Equalized Explainability Through Data Reconstruction

Recent progress in machine learning has placed a growing emphasis on explainability and fairness. However, many studies have confined efforts to leveraging explanatory techniques to promote model fairness, overlooking the essential fairness of the explanations. This study addresses this gap by proposing a novel principle of equalized explainability, which fulfills fair and uniform explanations across various demographic groups. To this end, we introduce a quantitative measure for assessing the explanation disparity leveraging Explainable Artificial Intelligence (XAI) tools. To achieve equalized explainability, we propose a reconstruction framework including modules for data reconstruction, equalization of explanations, and performance preservation. Experiments using real-world datasets demonstrate this framework’s effectiveness in securing equitable and consistent explanations across different groups, as well as achieving trade-offs between fairness and explanation.