Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions
This work addresses the need for domain experts to trust and improve ML models through interactive explanations, though it is incremental as it builds on existing explanation methods like Occlusion and Shapley values.
The paper tackles the problem of correcting model-agnostic feature attributions when they contradict expert knowledge, by implementing interactive methods to enforce different attributions and using corrected explanations to generate extra local data for retraining, resulting in significant performance improvements and increased sample efficiency in active learning settings.
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.