  The use of models, even if efficient, must be accompanied by an understanding
at all levels of the process that transforms data (upstream and downstream).
Thus, needs increase to define the relationships between individual data and
the choice that an algorithm could make based on its analysis (e.g. the
recommendation of one product or one promotional offer, or an insurance rate
representative of the risk). Model users must ensure that models do not
discriminate and that it is also possible to explain their results. This paper
introduces the importance of model interpretation and tackles the notion of
model transparency. Within an insurance context, it specifically illustrates
how some tools can be used to enforce the control of actuarial models that can
nowadays leverage on machine learning. On a simple example of loss frequency
estimation in car insurance, we show the interest of some interpretability
methods to adapt explanation to the target audience.
