Evaluating Robustness of Counterfactual Explanations¶
Artelt, A., Vaquet, V., Velioglu, R., Hinder, F., Brinkrolf, J., Schilling, M., & Hammer, B. (2021). Evaluating Robustness of Counterfactual Explanations. 2021 IEEE Symposium Series on Computational Intelligence, 1-9.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Nearby-World Sensitivity Review
- Its strength is that it separates a robust counterfactual claim from a fragile one — the central worry behind counterfactual explanations in automated decisions, which are only actionable if they are stable to small, plausible changes in the inputs.
This sourceShows that counterfactual explanations can be tested for robustness to small input perturbations and that unstable recommendations can change drastically for similar cases, undermining their practical actionability and fairness.
- Its strength is that it separates a robust counterfactual claim from a fragile one — the central worry behind counterfactual explanations in automated decisions, which are only actionable if they are stable to small, plausible changes in the inputs.
Verification¶
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Was it audited? Yes. A second, independent pass read the citation against the article text and recorded a verdict.
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