One Explanation to Rule them All -- Ensemble Consistent Explanations
This addresses the need for transparency in complex AI systems that rely on ensembles, making explanations more useful and understandable for users.
The paper tackles the problem of explaining ensembles of decisions in AI systems by proposing a novel concept for generating a single consistent explanation, rather than multiple conflicting ones, using counterfactual explanations as an implementation.
Transparency is a major requirement of modern AI based decision making systems deployed in real world. A popular approach for achieving transparency is by means of explanations. A wide variety of different explanations have been proposed for single decision making systems. In practice it is often the case to have a set (i.e. ensemble) of decisions that are used instead of a single decision only, in particular in complex systems. Unfortunately, explanation methods for single decision making systems are not easily applicable to ensembles -- i.e. they would yield an ensemble of individual explanations which are not necessarily consistent, hence less useful and more difficult to understand than a single consistent explanation of all observed phenomena. We propose a novel concept for consistently explaining an ensemble of decisions locally with a single explanation -- we introduce a formal concept, as well as a specific implementation using counterfactual explanations.