Summary of Statistics and Explainability: a Fruitful Alliance, by Valentina Ghidini
Statistics and explainability: a fruitful alliance
by Valentina Ghidini
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 using standard statistical tools to address explainability issues in AI models. It argues that statistical estimators can provide theoretical guarantees and evaluation metrics for assessing explanation quality, replacing subjective human assessment. The approach also enables uncertainty quantification through classical statistics, enhancing explanation robustness and trustworthiness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses math and stats to make AI explanations more reliable and trustworthy. Instead of relying on people’s opinions, the paper shows how statistical tools can help define what an explanation is, measure its quality, and even handle tricky situations like “what if” scenarios. It’s a step towards making AI models more transparent and accountable. |
