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A collection of data, software, and papers from the field of explainable AI.

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An open-source, explainable AI toolkit built for analytics and autonomy applications. Latest release v0.6.1


Explore papers from the latest state-of-the-art research in explainable AI.



After Action Review for AI (AARfAI)

Our visual analytics tool, enhanced with an AARfAI workflow, allows domain experts to navigate an AI’s reasoning process in a systematic way to quickly find ...

Explainable VQA with SOBERT

Demo several capabilities of the Spatial-Object Attention BERT (SOBERT) Visual Question Answering (VQA) model with BERT and ErrorCam attention maps.

Remote Controlled Studies with Humans

This paper describes strategies for dealing with issues that came into play when doing two research studies with remote human subjects in the COVID era.

Bayesian Teaching for XAI

Bayesian teaching provides a human-centered, theoretical framework for XAI based on cognitive science. We showcase the framework’s applicability in domains s...

Curiosity Checklist

The purpose of the Curiosity Checklist is to enable researchers to gain a quick look at why a user wants an explanation.

Explanation Scorecard

The Scorecard presents a number of Levels of explanation. At the lower levels are explanations in the terms of the cues or features of individual instances. ...

Explanation Goodness Checklist

The Explanation Goodness Checklist is intended to be used by researchers or their XAI systems that have created explanations. The Explanation Goodness Checkl...

Explanation Satisfaction Scale

Explanation Satisfaction is an evaluation of explanations by a user. The reference is to the explanatory value to the user.

Similarity Based Saliency Maps

Similarity Based Saliency Maps (SBSM) is a similarity based saliency algorithm that utilizes standard distance metrics to compute image regions that result i...

Stakeholder Playbook

The Playbook lists the explanation requirements of jurisprudence professionals, Contracting Officers, Procurement Officers, Program Managers, Development Te...


Juice is a library for learning tractable probabilistic circuits from data, and using the learned models to build solutions in explainable AI, robustness to ...