  In recent years, a large number of XAI (eXplainable Artificial Intelligence)
solutions have been proposed to explain existing ML (Machine Learning) models
or to create interpretable ML models. Evaluation measures have recently been
proposed and it is now possible to compare these XAI solutions. However,
selecting the most relevant XAI solution among all this diversity is still a
tedious task, especially when meeting specific needs and constraints. In this
paper, we propose AutoXAI, a framework that recommends the best XAI solution
and its hyperparameters according to specific XAI evaluation metrics while
considering the user's context (dataset, ML model, XAI needs and constraints).
It adapts approaches from context-aware recommender systems and strategies of
optimization and evaluation from AutoML (Automated Machine Learning). We apply
AutoXAI to two use cases, and show that it recommends XAI solutions adapted to
the user's needs with the best hyperparameters matching the user's constraints.
