A Principled Evaluation Framework for Neuron Explanations
Keywords: interpretability, mechanistic interpretability, explainable AI, trustworthy machine learning
TL;DR: We unify methods for evaluating neuron explanations under one framework and show many popular methods fail basic sanity checks.
Abstract: Understanding the function of individual units in a neural network is an important building block for mechanistic interpretability. This is often done by generating a simple text explanation of the behavior of individual neurons or units. However, for these explanations to be useful, we must understand how reliable and truthful they are. In this work we unify many existing explanation evaluation methods under one mathematical framework. This allows us to compare and contrast existing evaluation metrics and understand the evaluation pipeline with increased clarity. We propose two simple sanity checks on the evaluation metrics and show that many commonly used metrics fail these tests and do not change their score after massive changes to the concept labels. Based on our experimental and theoretical results, we propose guidelines that future evaluations should follow and identify good evaluation metrics such as correlation.
Primary Area: interpretability and explainable AI
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Submission Number: 13311
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