CEL: Comprehensive Counterfactual Explanations Library and Benchmark

Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome. While early methods primarily focused on minimal feature changes, recent work incorporates additional properties such as sparsity, actionability and plausibility. Despite this progress, fair and systematic evaluation remains challenging. Existing studies often rely on different data splits, pr

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ethics.ai (24 July 2026), “CEL: Comprehensive Counterfactual Explanations Library and Benchmark,” evidence record 16135, https://ethics.ai/record/16135 (originally published by arXiv fairness query).

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