Counterfactual Training: Teaching Models Plausible and Actionable Explanations
This addresses the need for more interpretable and robust AI systems in real-world decision-making, representing a novel approach rather than an incremental improvement.
The paper tackles the problem of making machine learning models produce plausible and actionable counterfactual explanations by introducing a novel training regime that incorporates counterfactuals during training, resulting in models that inherently generate desirable explanations and show improved adversarial robustness.
We propose a novel training regime termed counterfactual training that leverages counterfactual explanations to increase the explanatory capacity of models. Counterfactual explanations have emerged as a popular post-hoc explanation method for opaque machine learning models: they inform how factual inputs would need to change in order for a model to produce some desired output. To be useful in real-world decision-making systems, counterfactuals should be plausible with respect to the underlying data and actionable with respect to the feature mutability constraints. Much existing research has therefore focused on developing post-hoc methods to generate counterfactuals that meet these desiderata. In this work, we instead hold models directly accountable for the desired end goal: counterfactual training employs counterfactuals during the training phase to minimize the divergence between learned representations and plausible, actionable explanations. We demonstrate empirically and theoretically that our proposed method facilitates training models that deliver inherently desirable counterfactual explanations and additionally exhibit improved adversarial robustness.