Fooling LIME and SHAP¶
Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. Proceedings of the 2020 AAAI/ACM Conference on AI, Ethics, and Society, 180-186.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Explainability Review
- This is more dangerous than opacity because it feels like understanding while delivering none; feature-attribution methods such as LIME and SHAP are useful precisely to the extent their faithfulness is checked rather than assumed.
This sourceDemonstrates that LIME and SHAP can produce plausible feature attributions that do not faithfully reflect a model's actual behavior.
- This is more dangerous than opacity because it feels like understanding while delivering none; feature-attribution methods such as LIME and SHAP are useful precisely to the extent their faithfulness is checked rather than assumed.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:31efe7719568 · see in the full table