Xplique: A Deep Learning Explainability Toolbox
This provides a practical tool for researchers to enhance model interpretability, though it is incremental as it aggregates existing methods.
The authors tackled the challenge of making advanced machine-learning models more scrutable by developing Xplique, a software library that includes representative explainability methods and evaluation metrics, which is freely available under the MIT license.
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.