  With the advent of Deep Learning, the field of machine learning (ML) has
surpassed human-level performance on diverse classification tasks. At the same
time, there is a stark need to characterize and quantify reliability of a
model's prediction on individual samples. This is especially true in
application of such models in safety-critical domains of industrial control and
healthcare. To address this need, we link the question of reliability of a
model's individual prediction to the epistemic uncertainty of the model's
prediction. More specifically, we extend the theory of Justified True Belief
(JTB) in epistemology, created to study the validity and limits of
human-acquired knowledge, towards characterizing the validity and limits of
knowledge in supervised classifiers. We present an analysis of neural network
classifiers linking the reliability of its prediction on an input to
characteristics of the support gathered from the input and latent spaces of the
network. We hypothesize that the JTB analysis exposes the epistemic uncertainty
(or ignorance) of a model with respect to its inference, thereby allowing for
the inference to be only as strong as the justification permits. We explore
various forms of support (for e.g., k-nearest neighbors (k-NN) and l_p-norm
based) generated for an input, using the training data to construct a
justification for the prediction with that input. Through experiments conducted
on simulated and real datasets, we demonstrate that our approach can provide
reliability for individual predictions and characterize regions where such
reliability cannot be ascertained.
