  We propose a novel method for explaining the predictions of any classifier.
In our approach, local explanations are expected to explain both the outcome of
a prediction and how that prediction would change if 'things had been
different'. Furthermore, we argue that satisfactory explanations cannot be
dissociated from a notion and measure of fidelity, as advocated in the early
days of neural networks' knowledge extraction. We introduce a definition of
fidelity to the underlying classifier for local explanation models which is
based on distances to a target decision boundary. A system called CLEAR:
Counterfactual Local Explanations via Regression, is introduced and evaluated.
CLEAR generates w-counterfactual explanations that state minimum changes
necessary to flip a prediction's classification. CLEAR then builds local
regression models, using the w-counterfactuals to measure and improve the
fidelity of its regressions. By contrast, the popular LIME method, which also
uses regression to generate local explanations, neither measures its own
fidelity nor generates counterfactuals. CLEAR's regressions are found to have
significantly higher fidelity than LIME's, averaging over 45% higher in this
paper's four case studies.
