  Nowadays, the use of machine learning models is becoming a utility in many
applications. Companies deliver pre-trained models encapsulated as application
programming interfaces (APIs) that developers combine with third party
components and their own models and data to create complex data products to
solve specific problems. The complexity of such products and the lack of
control and knowledge of the internals of each component used cause unavoidable
effects, such as lack of transparency, difficulty in auditability, and
emergence of potential uncontrolled risks. They are effectively black-boxes.
Accountability of such solutions is a challenge for the auditors and the
machine learning community. In this work, we propose a wrapper that given a
black-box model enriches its output prediction with a measure of uncertainty.
By using this wrapper, we make the black-box auditable for the accuracy risk
(risk derived from low quality or uncertain decisions) and at the same time we
provide an actionable mechanism to mitigate that risk in the form of decision
rejection; we can choose not to issue a prediction when the risk or uncertainty
in that decision is significant. Based on the resulting uncertainty measure, we
advocate for a rejection system that selects the more confident predictions,
discarding those more uncertain, leading to an improvement in the trustability
of the resulting system. We showcase the proposed technique and methodology in
a practical scenario where a simulated sentiment analysis API based on natural
language processing is applied to different domains. Results demonstrate the
effectiveness of the uncertainty computed by the wrapper and its high
correlation to bad quality predictions and misclassifications.
