Beyond Post-Hoc Instance-Based Explanation Methods

Abstract

With the increasing demand for understanding the decision-making processes of artificial intelligence applications, explainable AI (XAI) systems have become increasingly important. Counterfactual explana- tions, a promising approach in XAI, take advantage of human counterfactual reasoning mechanisms to offer intuitive explanations of how a model’s predictions could have been different. This Ph.D. project focuses on the design of post-hoc XAI techniques to generate counterfactual explanations that utilize case-based reasoning. It highlights the benefits of post-hoc explanation systems in improving our un- derstanding of black-box models and explores the unique advantages of counterfactual explanations as an instance-based method. Furthermore, this report presents an overview of my doctoral studies and current state. It contributes to the growing body of research on XAI by presenting novel insights into the design of post-hoc XAI systems. Additionally, the report identifies areas in the existing literature that require further investigation and suggests potential directions for future research. Overall, this report offers valuable insights for researchers and practitioners interested in the design of XAI systems and highlights the importance of transparency and interpretability in artificial intelligence.

Beyond Post-Hoc Instance-Based Explanation Methods