  While explainability is a desirable characteristic of increasingly complex
black-box models, modern explanation methods have been shown to be inconsistent
and contradictory. The semantics of explanations is not always fully understood
- to what extent do explanations "explain" a decision and to what extent do
they merely advocate for a decision? Can we help humans gain insights from
explanations accompanying correct predictions and not over-rely on incorrect
predictions advocated for by explanations? With this perspective in mind, we
introduce the notion of dissenting explanations: conflicting predictions with
accompanying explanations. We first explore the advantage of dissenting
explanations in the setting of model multiplicity, where multiple models with
similar performance may have different predictions. In such cases, providing
dissenting explanations could be done by invoking the explanations of
disagreeing models. Through a pilot study, we demonstrate that dissenting
explanations reduce overreliance on model predictions, without reducing overall
accuracy. Motivated by the utility of dissenting explanations we present both
global and local methods for their generation.
