Semifactual Explanation

Semifactual explanations, which use "even if" scenarios to illustrate robustness of outcomes, are emerging as a valuable tool in Explainable AI (XAI). Current research focuses on developing algorithms to generate these explanations for various machine learning models, including reinforcement learning agents, and exploring their computational complexity within formal frameworks like abstract argumentation. This work aims to improve user understanding and trust in AI systems by providing insights into decisions beyond simple cause-and-effect, particularly focusing on optimizing positive outcomes and explaining rejections. The ultimate goal is to enhance the transparency and usability of AI systems across diverse applications.