  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/).
