Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers

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The increasing use of machine learning in practice and legal regulations like EU’s GDPR cause the necessity to be able to explain the prediction and behavior of machine learning models. A prominent example of particularly intuitive explanations of AI models in the context of decision making are counterfactual explanations. Yet, it is still an open research problem how to efficiently compute counterfactual explanations for many models. In this contribution, we investigate how to efficiently compute counterfactual explanations for an important class of models, prototype-based classifiers such as learning vector quantization models. In particular, we derive specific convex and non-convex programs depending on the used metric. Typically counterfactual explanations deliver a feedback in terms of changes of the input features which lead to a different output – one application scenario is the link of these required changes to actionable items to change the desired outcome. Yet, rather than minimum changes of the input, it is interesting to address minimum changes of the model itself, which are required to lead to a different result. rather than a change of its inputs. We phrase this question as a counterfactual of the model prescription rather than the data points. We focus on distance-based classifiers (in particular learning vector quantization models), where model changes correspond to changes of metric parameters, and we develop efficient optimization techniques to generate such counterfactual metric changes depending on the chosen model.