  Today's most advanced machine-learning models are hardly scrutable. The key
challenge for explainability methods is to help assisting researchers in
opening up these black boxes, by revealing the strategy that led to a given
decision, by characterizing their internal states or by studying the underlying
data representation. To address this challenge, we have developed Xplique: a
software library for explainability which includes representative
explainability methods as well as associated evaluation metrics. It interfaces
with one of the most popular learning libraries: Tensorflow as well as other
libraries including PyTorch, scikit-learn and Theano. The code is licensed
under the MIT license and is freely available at github.com/deel-ai/xplique.
