On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective

2023-04-26 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, short

Summary

The RemOve-And-Retrain (ROAR) benchmark, commonly used for evaluating feature attribution methods, exhibits significant validity pitfalls from an information-theoretic standpoint. Research shows that even model- and data-agnostic post-processing of attribution maps, which cannot add information due to the data processing inequality, can artificially inflate ROAR scores. This means a higher ROAR ranking does not inherently indicate that an attribution map contains more relevant information about a model's decision function. This failure mode stems from a bias towards spatially blurry masks. Experiments conducted on CIFAR-10, SVHN, and CUB-200 datasets consistently demonstrate a link between blurriness and improved ROAR performance, a trend also observed in the ROAD variant. These findings provide crucial guidelines for more cautious removal-based benchmarking and have implications for validating the mechanistic understanding of neural network internals.

Key takeaway

For AI Scientists and Machine Learning Engineers evaluating feature attribution methods, you should critically assess ROAR benchmark results. Do not rely solely on improved ROAR rankings, as these can be artificially inflated by post-processing that introduces blurriness, without adding actual information. You must incorporate information-theoretic principles and consider diverse evaluation metrics to ensure a robust and valid assessment of attribution method performance and mechanistic understanding.

Key insights

ROAR benchmark scores can be misleadingly improved by information-agnostic post-processing, indicating a bias towards blurriness.

Principles

• Data Processing Inequality limits information gain.
• Blurriness can artificially inflate ROAR scores.
• ROAR ranking alone is not sufficient evidence.

Method

The paper identifies a failure mode in ROAR benchmarking where post-processing, even without adding information, improves scores due to a bias towards spatially blurry masks.

In practice

• Evaluate attribution methods beyond ROAR scores.
• Consider blurriness bias in benchmark design.
• Use information-theoretic validation.

Topics

• ROAR Benchmark
• Feature Attribution
• Information Theory
• Neural Network Interpretability
• Model Evaluation
• Data Processing Inequality

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.