Explainable AI Cheat Sheet
Cheat sheet
Video
A brief overview of the Explainable AI cheat sheet with examples.
Resources
If you're interested to learn more, this is a non-exhaustive list of links and resources. We'll continue to add more resources. Feel free to suggest valuable resources on the Issues page.
General
Interpretable Machine Learning (IML) - Christoph Molnar
Explainability for NLP - Isabelle Augenstein [video]
NLP Highlights: Interpreting NLP Model Predictions - Sameer Singh [audio]
Please Stop Doing "Explainable" ML - Cynthia Rudin [video]
Interpretable Models
StatQuest: K-nearest neighbors, Clearly Explained [video]
IML: Chapter 4 [online book]
Model agnostic methods
IML: Chapter 3 [online book]
SHAP [software]
Model specific methods
Axiomatic Attribution for Deep Networks - Integrated Gradients [paper]
Attention is not Explanation [paper]
Attention is not not Explanation [paper]
A Benchmark for Interpretability Methods in Deep Neural Networks [paper]
Sanity Checks for Saliency Maps [paper]
Example based methods
IML: Chapter 6: Example-Based Explanations [online book]
Neural representation methods
Feature Visualization [article]
Multimodal Neurons in Artificial Neural Networks more recent feature visualization work [article]
SVCCA,
PWCCA [papers]
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [paper]
What you can cram into a single $&!#* vector:
Probing sentence embeddings for linguistic properties [paper]
Other methods
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
[paper]
Pitfalls
The Mythos of Model Interpretability
[paper]
Towards falsifiable interpretability research
[paper]
