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Found 6 papers, 2 papers with code
Advancing Post Hoc Case Based Explanation with Feature Highlighting
Explainable AI (XAI) has been proposed as a valuable tool to assist in downstream tasks involving human and AI collaboration.
Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals
Counterfactual explanations are an increasingly popular form of post hoc explanation due to their (i) applicability across problem domains, (ii) proposed legal compliance (e. g., with GDPR), and (iii) reliance on the contrastive nature of human explanation.
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ
Counterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems due to their psychological validity, flexibility across problem domains and proposed legal compliance.
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
Whilst an abundance of techniques have recently been proposed to generate counterfactual explanations for the predictions of opaque black-box systems, markedly less attention has been paid to exploring the uncertainty of these generated explanations.
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques
In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI).
Instance-based Counterfactual Explanations for Time Series Classification
In recent years, there has been a rapidly expanding focus on explaining the predictions made by black-box AI systems that handle image and tabular data.
