IJCAI-2019: Twin-Systems to Explain Artificial Neural Networks Using Case-Based Reasoning

This repository is the official implementation for the above IJCAI paper.

An updated version of the algorithm and user studies was recently published at the Artificial Intelligence Journal. It is recommended you refer to this article. I made a colab file which you can use to see the most up-to-date version of the twin system algorithm here:

SEE UP-TO-DATE VERSION OF TWIN-SYSTEM ALGORITHM HERE

Requirements

To install requirements:

python3 -m venv myenv
source myenv/bin/activate
pip install -r requirements.txt

Generating An Explanation

Follow the notebooks one by one to go through the experiments and recreate the results of the main paper.

Results

Table of results from experiment 2 using CNNs:

Cite Bibtext

@inproceedings{ijcai2019-376, title = {Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI}, author = {Kenny, Eoin M. and Keane, Mark T.}, booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI-19}}, publisher = {International Joint Conferences on Artificial Intelligence Organization},
pages = {2708--2715}, year = {2019}, month = {7}, doi = {10.24963/ijcai.2019/376}, url = {https://doi.org/10.24963/ijcai.2019/376}, }

}