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.

翻译：当今最先进的机器学习模式几乎难以分辨。 解释方法的关键挑战在于帮助研究人员打开这些黑盒,通过披露导致做出特定决定的战略,通过说明其内部状态或研究基本数据表述。 为了应对这一挑战,我们开发了Xplique:一个解释性软件库,其中包括有代表性的解释性方法和相关的评估指标。它与最受欢迎的学习图书馆之一:Tensorplow以及包括PyTorch、Scikit-learn和Theano在内的其他图书馆连接。 该代码根据麻省理工学院的许可证获得许可,可在 github.com/deel-ai/xplique免费查阅。