arXiv:2304.13836v5 Announce Type: replace-cross Abstract: The RemOve-And-Retrain (ROAR) benchmark is widely used to evaluate feature attribution methods, yet its validity remains underexplored from an information-theoretic perspective. We show that model- and data-agnostic post-processing of attribution maps (transformations that, by the data processing inequality, \emph{cannot} add information about the decision function) can often improve ROAR scores. This means that an improved ROAR ranking is not, by itself, evidence that an attribution map carries more information about the model. We trac
The paper highlights current limitations in a widely used benchmark for AI feature attribution methods, suggesting a growing need for more robust evaluation frameworks as AI interpretability becomes more critical.
It challenges the reliability of a standard AI evaluation metric, implying that current assessments of AI interpretability may be flawed and decisions based on them could be misinformed.
Researchers and practitioners will need to re-evaluate how they assess and compare feature attribution methods, potentially leading to the development of new, more robust benchmarks.
- · AI interpretability researchers
- · Developers of new AI evaluation metrics
- · Users prioritizing trustworthy AI
- · AI models reliant on ROAR scores for validation
- · Researchers using ROAR without critical assessment
- · Less robust AI interpretability methods
The validity of existing feature attribution method comparisons derived from ROAR could be questioned, prompting re-analysis.
New theoretical and empirical work will emerge to create better, information-theoretically sound evaluation benchmarks for AI interpretability.
Improved interpretability evaluation could lead to more reliable and deployable AI systems, particularly in sensitive applications, but also potentially slow down progress as methods are rigorously re-examined.
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Read at arXiv cs.AI