Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind
predictions is, however, quite important in assessing trust,
which is fundamental if one plans to take action based on a
prediction, or when choosing whether to deploy a new model.
Such understanding also provides insights into the model,
which can be used to transform an untrustworthy model or
prediction into a trustworthy one.
In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable
model locally around the prediction. We also propose a
method to explain models by presenting representative individual predictions and their explanations in a non-redundant
way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by
explaining different models for text (e.g. random forests)
and image classification (e.g. neural networks). We show the
utility of explanations via novel experiments, both simulated
and with human subjects, on various scenarios that require
trust: deciding if one should trust a prediction, choosing
between models, improving an untrustworthy classifier, and
identifying why a classifier should not be trusted.
Author(s): Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
Publication Date: 2016
Publication Site: kdd, Association for Computing Machinery