Increasing Performance And Sample Efficiency With Model-agnostic
  Interactive Feature Attributions
Joran Michiels Maarten De Vos Johan Suykens
Model-agnostic feature attributions can provide local insights in complex ML
models. If the explanation is correct, a domain expert can validate and trust
the model's decision. However, if it contradicts the expert's knowledge,
related work only corrects irrelevant features to improve the model. To allow
for unlimited interaction, in this paper we provide model-agnostic
implementations for two popular explanation methods (Occlusion and Shapley
values) to enforce entirely different attributions in the complex model. For a
particular set of samples, we use the corrected feature attributions to
generate extra local data, which is used to retrain the model to have the right
explanation for the samples. Through simulated and real data experiments on a
variety of models we show how our proposed approach can significantly improve
the model's performance only by augmenting its training dataset based on
corrected explanations. Adding our interactive explanations to active learning
settings increases the sample efficiency significantly and outperforms existing
explanatory interactive strategies. Additionally we explore how a domain expert
can provide feature attributions which are sufficiently correct to improve the
model.