  We propose a new method based on machine learning to \emph{play the devil's
advocate} and investigate the impact of unknown systematic effects in a
quantitative way. This method proceeds by reversing the measurement process and
using the physics results to interpret systematic effects under the Standard
Model hypothesis. We explore this idea with two alternative approaches: the
first one relies on a combination of gradient descent and optimisation
techniques, its application and potentiality is illustrated with an example
that studies the branching fraction measurement of a heavy-flavour decay. The
second method employs reinforcement learning and it is applied to the
determination of the $P_{5}^{'}$ angular observable in $B^0 \to K^{*0}
\mu^+\mu^-$ decays. We find that for the former, the size of a hypothetical
hidden systematic uncertainty strongly depends on the kinematic overlap between
the signal and normalisation channel, while the latter is very robust against
possible mismodellings of the efficiency.
