Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Abstract
The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus—a comprehensive, evaluation toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://github.com/understandable-machine-intelligence-lab/Quantus/).
Cite
Text
Hedström et al. "Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond." Machine Learning Open Source Software, 2023.Markdown
[Hedström et al. "Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond." Machine Learning Open Source Software, 2023.](https://mlanthology.org/mloss/2023/hedstrom2023jmlr-quantus/)BibTeX
@article{hedstrom2023jmlr-quantus,
title = {{Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond}},
author = {Hedström, Anna and Weber, Leander and Krakowczyk, Daniel and Bareeva, Dilyara and Motzkus, Franz and Samek, Wojciech and Lapuschkin, Sebastian and Höhne, Marina M.-C.},
journal = {Machine Learning Open Source Software},
year = {2023},
pages = {1-11},
volume = {24},
url = {https://mlanthology.org/mloss/2023/hedstrom2023jmlr-quantus/}
}