Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge

arXiv:2609.15609 · cs.AI · Submitted 2026-09-14 · Read on arXiv

cs.AI

Submitted: 2026-09-14

Updated: 2026-09-14

Journal ref: The 17th International Conference on Scalable Uncertainty Management (SUM 2026), Oct 2026, Athens, Greece

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Sparsity — 11× in this paper · explained in 4 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Structural Constraints — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Actionability — 1× in this paper · explained in 3 episodes
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Xplainable Artificial Intelligence — 1× in this paper

Sources

• Explaining and Harnessing Adversarial Examples
• Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

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