awesome-xai

Adaptive semantice inpute sampling for explanation.

Accumulated local effects plot.

Autoencoder Based Approach for Local Interpretability.

High-Precision Model-Agnostic Explanations.

Authors perform a series of NLP experiments which argue attention does not provide meaningful explanations. They also demosntrate that different attentions can generate similar model outputs.

This is a rebutal to the above paper. Authors argue that multiple explanations can be valid and that the and that attention can produce

A debate on whether interpretability is necessary for ML with Rich Caruana and Patrice Simard for and Kilian Weinberger and Yann LeCun against.

A toolkit for XAI which is focused exclusively on tabular data. It implements a variety of data and model evaluation techniques.

SHAP and LIME-based front-end explainer.

A tool for Tensorboard or Notebooks which allows investigating model performance and fairness.

A Python module for using Shapley Additive Explanations.

The man behind Explainable Boosting Machines.

A UK-based research center that performs research into ethical AI/ML, which frequently involves XAI.

One of the preeminent researchers in XAI.