The work "Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations", supported by iToBoS project, has been published.
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 exists that exhaustively and speedily allows researchers to quantitatively evaluate explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus — a comprehensive, open-source 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 open source license on PyPi (or on https://github.com/understandable-machine-intelligence-lab/quantus/).
Acknowledgments
This work was partly funded by the German Ministry for Education and Research through project Explaining 4.0 (ref. 01IS20055) and BIFOLD (ref. 01IS18025A and ref. 01IS18037A), the Investitionsbank Berlin through BerDiBA (grant no. 10174498), as well as the European Union’s Horizon 2020 programme through iToBoS (grant no. 965221).
Find out more at https://arxiv.org/pdf/2202.06861.pdf.