Introducing User Feedback-based Counterfactual Explanations (UFCE)

arXiv:2403.00011v1 Announce Type: new
Abstract: Machine learning models are widely used in real-world applications. However, their complexity makes it often challenging to interpret the rationale behind their decisions. Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in eXplainable Artificial Intelligence (XAI). CE provides actionable information to users on how to achieve the desired outcome with minimal modifications to the input. However, current CE algorithms usually operate within the entire feature space when optimizing changes to …

abstract applications artificial artificial intelligence arxiv ces complexity counterfactual cs.ai cs.hc cs.lg decisions explainable artificial intelligence feedback information intelligence machine machine learning machine learning models solution type user feedback world xai