explAIner

This repository contains the source code for explAIner -- the framework for explainable AI and interactive machine learning.

Architecture

The framework consists of four plugins, which represent the stages of explanation, namely

Understanding
Diagnosis
Refinement
Reporting

Repository Structure

Folders

The repository contains 4 folders:

backend/ This folder contains the python backend for the high-level (model in-/output) explanations.
tensorboard-explainer-plugin/
This folder contains the actual explAIner code. It has the following sub-folders:
- explainer_plugins/
  - _1_understanding/
    Plugin for understanding. Data-independent explanations.
  - _2_diagnosis/
    Plugin for diagnosis. Debugging of NN graph.
  - _3_refinement/
    Plugin for refinement. Recommendations on improvements.
  - _4_reporting/
    Plugin for reporting. Summarizes the findings from previous steps.
  - common/
    Parts that are used in more than one plugin.
- explainer_tensorboard/
  The modified TensorBoard executable, with explAIner plugins injected.

Building and Starting

To create example logs for explAIner, run the following command and wait for it to finish:

docker-compose up --build --remove-orphans explainer_summary

To build and start the explAIner TensorBoard executable (together with custom backend servers):

docker-compose up --build --remove-orphans -d explainer_tensorboard

Although the containers should be up and running after a few seconds, it might take a while until the code is fully compiled and the system gets available under http://127.0.0.1:6006.

Citing this Repository

To reference this repository, please cite the original explAIner publication (pre-print available on arXiv.org):

T. Spinner, U. Schlegel, H. Schafer, and M. El-Assady, “explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning,” IEEE Trans. on Vis. and Computer Graphics, vol. 26, no. 1, Art. no. 1, 2020, doi: 10.1109/tvcg.2019.2934629.

BibTeX

@ARTICLE{SpinnerEtAl2020,
  author = {Thilo Spinner and Udo Schlegel and Hanna Schäfer and Mennatallah El-Assady},
  title = {{explAIner}: A Visual Analytics Framework for Interactive and Explainable Machine Learning},
  journal = {{IEEE} Transactions on Visualization and Computer Graphics},
  year = {2020},
  volume = {26},
  number = {1},
  pages = {1064-1074},
  doi = {10.1109/TVCG.2019.2934629},
}

pombredanne / explainer