Team |
Approach |
Application |
UC Berkeley
|
Salience-map attention mechanisms implemented in DNNs
Petsiuk 2021
Vasu et al. 2021
|
Saliency maps for object detectors allow users to identify the detector
which will be more accurate by reviewing sample detections & maps
Petsiuk 2021
|
|
Transduction of DNN states into natural language explanations
Hendricks et al. 2021
|
Explainable and advisable autonomous driving systems to fill in
knowledge gaps. Humans can evaluate AI-generated explanations for
navigation decisions.
Kim et al. 2021
Watkins et al. 2021
|
Charles River Analytics
|
Causal models of deep reinforcement learning policies to enable
explanation-enhanced training by answering counterfactual queries
Druce et al. 2021
Witty et al. 2021
|
Human-machine teaming gameplay in StarCraft2
|
|
|
Developed a distilled version of a pedestrian detection model, which
used convolutional auto encoders to condense the activations into user
understandable “chunks”. |
Carnegie Mellon University
|
Robustified classifiers with salient gradients
Yeh and Ravikumar 2021
|
Interactive debugger interface for visualizing poisoned training
datasets. Work is applied on the IARPA TrojAI dataset.
Sun et al. 2021
|
Oregon State University
|
iGOS++ visual saliency algorithm
Khorram et al. 2021
|
Debugging of COVID-19 diagnosis chest x-ray classifier
|
|
Quantized bottleneck networks for deep RL algorithms
|
Understanding recurrent policy networks through extracted state machines
and key decision points in video games and control
Danesh et al. 2021
|
|
Explanation analysis process for reinforcement learning systems
Dodge et al. 2021
|
After-action review of AI decisions mirror the army’s after action
review system to understand why AI made its decisions to improve
explainability and AI trust
Mai et al. 2020
|
|
Reinforcement learning model via embedded self-predictions
|
Contrastive explanations of action choices in terms of human
understandable properties of future outcomes
Lin et al., 2021
|
Rutgers University
|
Bayesian teaching to select examples and features from the training data
to explain model inferences to a domain expert
Yang et al. 2021
|
Interactive tool for analyzing a pneumothorax detector for chest x-rays.
Targeted user study engaging ~10 radiologists
demonstrated the effectiveness of the explanations.
Folke et al. 2021
|
Team |
Approach |
Application |
UT Dallas
|
Tractable probabilistic logic models where local explanations are
queries over probabilistic models and global explanations are generated
using logic, probability and directed trees and graphs
|
Activity recognition in videos using TACoS cooking tasks and WetLab
scientific lab procedure datasets. Generates explanations about whether
activities are present in the video data.
Chiradeep et al. 2021
|
PARC
|
Reinforcement learning implementing a hierarchical multifactor framework
for decision problems.
|
Simulated drone flight mission planning task where users learned to
predict each agent’s behavior to choose the best flight plan. User study
tested the usefulness of AI-generated local and global explanations in
helping users predict AI behavior.
Stefik et al. 2021
|
SRI
|
Spatial attention VQA (SVQA) and spatial-object attention BERT VQA
(SOBERT)
Ray et al. 2021
Alipour et al. 2021
|
Attention-based (gradCAM) explanations for MRI brain-tumor segmentation.
Visual salience models for video Q&A.
|
Raytheon BBN
|
- CNN based one-shot detector, using network dissection to identify the
most salient features
Bau et al. 2018
- Explanations produced by heatmaps and text explanations
Selvaraju et al. 2017
- Human-machine common ground modeling
|
-Indoor navigation with a robot (in collaboration with GA Tech)
- Video Q&A
- Human-assisted one-shot classification system by identifying the most
salient features
Ferguson et al. 2021
|
Texas A&M
|
Mimic learning methodology to detect falsified text.
Yuan et al. 2021
Linder et al. 2021
|
News claim truth classification
|
UCLA
|
CX-ToM Framework:
A new XAI framework using Theory-of-Mind where we pose explanation as an
iterative communication process, i.e. dialog, between the machine and
human user. In addition, we replace the standard attention based
explanations with novel counterfactual explanations called fault-lines.
Akula et al. 2021
Akula et al. 2020
|
Image Classification, Human body pose estimation.
|
|
A learning framework to acquire interpretable knowledge representation
and an Augmented Reality system for explanation interface.
Edmonds et al. 2019
Liu et al. 2021
|
Robot learning to open medicine bottles with locks and allows user
interventions to correct wrong behaviors.
|
|
Theory of mind explanation network with multi-level belief updates from
learning.
Edmonds et al. 2021 (in preparation)
|
Minesweeper-like game to find optimal path for an agent.
|
IHMC |
Explanation Scorecard |
Evaluate the utility of an explanation.
Defines seven levels of capability, from the null case of no
explanation, to surface features (e.g. heat maps), to AI introspections
such as choice logic, to diagnoses of the reasons for
failures. |
|
Cognitive Tutorial
|
A straightforward way to help users understand complex systems is to
provide a tutorial up front but the tutorial should not be restricted to
how the system works.
Hoffman and Clancey 2021
|
|
Stakeholder Playbook |
Survey of stakeholder needs, including
development team leaders, trainers, system developers and user team
leaders in industry and government. |
|
AI Evaluation Guidebook |
Identifies methodological shortcomings for
evaluating XAI techniques, spanning experimental design, control
conditions, experimental tasks and procedures, and statistical
methodologies. |