  Applying Artificial Intelligence (AI) and Machine Learning (ML) in critical
contexts, such as medicine, requires the implementation of safety measures to
reduce risks of harm in case of prediction errors. Spotting ML failures is of
paramount importance when ML predictions are used to drive clinical decisions.
ML predictive reliability measures the degree of trust of a ML prediction on a
new instance, thus allowing decision-makers to accept or reject it based on its
reliability. To assess reliability, we propose a method that implements two
principles. First, our approach evaluates whether an instance to be classified
is coming from the same distribution of the training set. To do this, we
leverage Autoencoders (AEs) ability to reconstruct the training set with low
error. An instance is considered Out-of-Distribution (OOD) if the AE
reconstructs it with a high error. Second, it is evaluated whether the ML
classifier has good performances on samples similar to the newly classified
instance by using a proxy model. We show that this approach is able to assess
reliability both in a simulated scenario and on a model trained to predict
disease progression of Multiple Sclerosis patients. We also developed a Python
package, named relAI, to embed reliability measures into ML pipelines. We
propose a simple approach that can be used in the deployment phase of any ML
model to suggest whether to trust predictions or not. Our method holds the
promise to provide effective support to clinicians by spotting potential ML
failures during deployment.
