Summary of Beexai: Benchmark to Evaluate Explainable Ai, by Samuel Sithakoul et al.
BEExAI: Benchmark to Evaluate Explainable AI
by Samuel Sithakoul, Sara Meftah, Clément Feutry
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper proposes a novel benchmarking tool called BEExAI to evaluate the quality and correctness of explainability methods for black-box machine learning models. The tool addresses the lack of a cohesive approach and consensus on evaluating the efficacy of post-hoc attribution methods, which is critical for ensuring the reliability of complex deep learning models in diverse data applications. The authors employ a set of selected evaluation metrics to enable large-scale comparison of different explainability methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explains that recent research has led to many post-hoc attribution methods for understanding black-box machine learning model outputs, but evaluating their quality lacks a clear approach and consensus on quantitative metrics. To address this, the authors develop BEExAI, a benchmark tool for comparing various post-hoc XAI methods using selected evaluation metrics. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
