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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.
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI
It is also unknown whether counterfactual explanations are equally effective for categorical as for continuous features, although current methods assume they do.
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