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Learn more about the history of the toolkit and our industry, academic, and government partners.
Capabilities
A collection of data, software, and papers from the field of explainable AI.
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Help to further advance the field of explainable AI by contributing new capabilities to the toolkit.
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Leverage explainable AI and find the right tool for the job by using our interactive concept map.
An open-source, explainable AI toolkit built for analytics and autonomy applications. Latest release v0.7.0
Publications
Explore papers from the latest state-of-the-art research in explainable AI.
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Counterfactual Explanations for Enhancing Human-Machine Teaming
This contribution provides a framework for generating counterfactual explanations for AI agents in the domain of StarCraft 2.
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 ...
The Consistent Visual Question Answering (ConVQA) Dataset
Dataset with metrics for quantitative evaluation of consistency in Visual Question Answering (VQA).
The Counterfactual Visual Question Answering (VQA) Dataset
Human curated counterfactual edits on VQA images for studying effective ways to produce counterfactual explanations.
SRI-DARE-BraTS explainable brain tumor segmentation
An interactive interface and APIs for segmenting brain tumors from fMRI scans and life expectancy prediction with deep attentional explanations and counterfa...
Generating Error Maps and Evaluating Attention and Error Maps
Measuring and improving attention map helpfulness in Visual Question Answering (VQA) with Error Maps.
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...
Explainable poisoned classifier identification
Demo of our XAI system built to detect backdoor poisoned classifiers with an adversarial approach.
Cognitive Models for Common Ground Modeling
We model both the AI and the human performer in a common modeling framework and use cognitive salience to reveal their respective mental models
Curiosity Checklist
The purpose of the Curiosity Checklist is to enable researchers to gain a quick look at why a user wants an explanation.
XAI Discovery Platform
Data set explorer to enable discovery and sensemaking and to support explanation of AI.
Explainable Question Answering System (EQUAS) demo system
Explainable Question Answering System one shot detector demo modality software.
Explaining Robot Behaviour
Algorithm to generate explanations from functional and mechanistic perspectives.
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. ...
Fault-Line Image Explanations
Algorithm for generating conceptual and counterfactual explanations for an image classification model.
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...
Integrated-Gradient Optimized Saliency Maps (iGOS++/I-GOS)
Integrated gradient-based saliency maps for multi-resolution explanation of deep networks.
Detecting Mis-Information with XAI
Our research investigates the effects of XAI assistants embedded in news review platforms for combating the propagation of mis-info.
Psychological Models of Explanatory Reasoning
Technical reports from Task Area 2 of the DARPA XAI program.
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...
Explainable AI self-driving controller
An introspective textual explanation model for self-driving cars and Berkeley Deep Drive-X Dataset
Multi-step salience with neural modular networks
Multi-step salience via sub-task decomposition for reasoning tasks
Datasets with multimodal explanations
Datasets with multimodal explanations for activity recognition (ACT-X) and visual question answering (VQA-X).
Natural Language Explanations for Fine-grained Image Classification
A method to produce visual explanations using natural language justifications.
Juice
Juice is a library for learning tractable probabilistic circuits from data, and using the learned models to build solutions in explainable AI, robustness to ...
Learning Tractable Interpretable Cutset Networks
This library helps the user learn tractable, interpretable cutset networks (a type of probabilistic model which combines decision trees and tree Bayesian net...
Explainable Video Activity Recognition Demo
Demo of our XAI system built to detect activities in videos with post-hoc explanations for its predictions.
Explainable Video Activity Search Tool
Demo of our explainable query-building tool to search activities within a collection of videos.
XDeep - A Post-Hoc Interpretation Package for Model Developers
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.