Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives

arXiv:2404.08721v1 Announce Type: new
Abstract: Explainable Artificial Intelligence (XAI) has emerged as a critical area of research aimed at enhancing the transparency and interpretability of AI systems. Counterfactual Explanations (CFEs) offer valuable insights into the decision-making processes of machine learning algorithms by exploring alternative scenarios where certain factors differ. Despite the growing popularity of CFEs in the XAI community, existing literature often overlooks the diverse needs and objectives of users across different applications and domains, leading to a lack of …

abstract ai systems algorithms artificial artificial intelligence arxiv beyond counterfactual cs.ai cs.lg decision explainable artificial intelligence insights intelligence interpretability machine machine learning machine learning algorithms making processes research systems transparency type xai