Interpretability Beyond Feature Attribution Information Guide

  1. Introduction to Interpretability Beyond Feature Attribution
  2. Main Features
  3. History
  4. Detailed Analysis
  5. Final Thoughts

Introduction to Interpretability Beyond Feature Attribution

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Main Features

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History

Information Challenging common interpretability assumptions in feature attribution explanations News
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AAAI 2022: Do Feature Attribution Methods Correctly Attribute Features
AAAI 2022: Do Feature Attribution Methods Correctly Attribute Features
Feature Attribution | Stanford CS224U Natural Language Understanding | Spring 2021
Feature Attribution | Stanford CS224U Natural Language Understanding | Spring 2021
Been Kim wants interpretability for everyone
Been Kim wants interpretability for everyone
Mechanistic Interpretability - NEEL NANDA (DeepMind)
Mechanistic Interpretability - NEEL NANDA (DeepMind)
The Science of Neural Networks (Intro to Mechanistic Interpretability)
The Science of Neural Networks (Intro to Mechanistic Interpretability)
What is interpretability
What is interpretability
The effectiveness of feature attribution methods & its correlation with automatic evaluation scores
The effectiveness of feature attribution methods & its correlation with automatic evaluation scores
Reading AI's Mind - Mechanistic Interpretability Explained [Anthropic Research]
Reading AI's Mind - Mechanistic Interpretability Explained [Anthropic Research]
Feature Attributions and Counterfactual Explanations Can Be Manipulated
Feature Attributions and Counterfactual Explanations Can Be Manipulated
25. Interpretability
25. Interpretability
MIT Deep Learning Genomics - Lecture 5 - Model Interpretability (Spring 2020)
MIT Deep Learning Genomics - Lecture 5 - Model Interpretability (Spring 2020)

Detailed Analysis

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Last Updated: October 29, 2026

Final Thoughts

Details PR-167: Interpretability Beyond Feature Attribution: Testing with Concept Activation Vector (TCAV) Guide
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Summary

Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim, Senior Research Scientist, Google Brain Presented at ... Paper arxiv.org/abs/2012.02748 Code git.sr.ht/~hyphaebeast/challenging-xai Demo ... Paper link: arxiv.org/abs/1711.11279 Presentation link: ... This video accompanies the paper "Do For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: stanford.io/ai To learn ... Been Kim is a staff research scientist at Google Brain. Her research focuses on improving 80000hours.org/mlst Visit our sponsor 80000 hours - grab their free career guide and their podcast! Use our ... A practical guide to neural nets, superposition, and expts around polysemanticity. Try Explain by Scrimba: ... A surprising fact about modern large language models is that nobody really knows how they work internally. At Anthropic, the ... Gradient now and redeem your free 5$ credits! gradient.1stcollab.com/bycloud Solving AI Doomerism: ... MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: Peter Szolovits View the complete course: ... MIT 6.874 Lecture 5. Spring 2020 Course website: mit6874.github.io/ Lecture slides: ...

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