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.

本文提供了两种流行的解释方法（Occlusion 和 Shapley 值）的模型无关实现，以无限制的交互方式实现不同的属性。利用已纠正的特征归属来生成额外的本地数据，通过在模拟和真实数据实验中展示我们提出的方法如何通过纠正的解释显著提高模型的性能。添加交互式解释以增加活动学习效率，显著优于现有的解释性交互策略。此外，本文探讨了领域专家如何提供足够正确的特征归属来改进模型。

模型无关的交互式特征归因提高性能和样本效率