SOTAVerified

Counterfactual Explanation

Returns a contrastive argument that permits to achieve the desired class, e.g., “to obtain this loan, you need XXX of annual revenue instead of the current YYY”

Papers

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Showing 151–177 of 177 papers

TitleStatusHype
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Integrating Prior Knowledge in Post-hoc Explanations—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Leveraging counterfactual concepts for debugging and improving CNN model performance—0
Towards LLM-guided Causal Explainability for Black-box Text Classifiers—0
Mining Action Rules for Defect Reduction Planning—0
Motif-guided Time Series Counterfactual Explanations—0
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations—0
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis—0
On the Relationship Between Counterfactual Explainer and Recommender—0
Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud—0
Private Counterfactual Retrieval—0
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies—0
Ranking Counterfactual Explanations—0
Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization—0
Robust Stochastic Graph Generator for Counterfactual Explanations—0
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks—0
Semi-supervised counterfactual explanations—0
TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation—0
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples—0
Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition—0
Towards Non-Adversarial Algorithmic Recourse—0
Transcending XAI Algorithm Boundaries through End-User-Inspired Design—0
Model-Agnostic Explanations using Minimal Forcing Subsets—0
Verified Training for Counterfactual Explanation Robustness under Data Shift—0
Very fast, approximate counterfactual explanations for decision forests—0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
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