Introducing User Feedback-based Counterfactual Explanations (UFCE) in Explainable AI
Machine learning models can be challenging to interpret, leading to the emergence of Counterfactual Explanations (CEs) in eXplainable Artificial Intelligence (XAI). The UFCE methodology addresses limitations in current CE algorithms and aims to provide actionable explanations based on user feedback.
Graph Edits for Counterfactual Explanations: A Comparative Study
Graph-based algorithms offer efficient and meaningful counterfactual explanations for image classifiers.
Generating Plausible and Actionable Counterfactual Explanations with Adversarial Random Forests
Adversarial random forests (ARF) can be leveraged to efficiently generate plausible counterfactual explanations that are also sparse and proximal to the original instance.
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimization
A method leveraging robust optimization techniques to generate counterfactual explanations that are provably robust to model parameter changes and plausible with respect to the training data distribution.
A Framework for Generating Feasible and Sparse Counterfactual Explanations Incorporating Causal Constraints
This work presents a framework for generating feasible and sparse counterfactual explanations that satisfy causal constraints, enabling the production of actionable insights for real-world applications.
Generating Likely Counterfactual Explanations Using Sum-Product Networks
A method for generating counterfactual explanations that are likely, similar to the factual, and sparse, by combining mixed-integer optimization with Sum-Product Networks to model the likelihood of counterfactuals.
