Summary of Revisiting the Robustness Of Post-hoc Interpretability Methods, by Jiawen Wei et al.
Revisiting the robustness of post-hoc interpretability methods
by Jiawen Wei, Hugues Turbé, Gianmarco Mengaldo
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes novel evaluation strategies for assessing the accuracy of post-hoc interpretability methods in explainable artificial intelligence (XAI). The existing methods provide a coarse-grained assessment, evaluating how the model’s performance degrades on average when corrupting different data points. However, this approach fails to provide a fine-grained assessment, measuring the robustness of post-hoc interpretability methods at the sample level. To address this limitation, the authors introduce an approach and two new metrics that enable a fine-grained evaluation. The results show that the robustness is generally linked to coarse-grained performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making artificial intelligence (AI) more understandable by creating ways to see how AI models work. Different methods for doing this can give different answers, so it’s important to know which ones are accurate. Most methods only look at how the model does on average when given bad data. But that doesn’t tell us if one method is better than another in a specific situation. The authors propose new ways to evaluate these methods and show that being good at understanding how AI works also means being good at handling unexpected situations. |
