Abstract
As neural networks increasingly make critical decisions in high-stakes settings, monitoring and explaining their behavior in an understandable and trustworthy manner is a necessity. One commonly used type of explainer is post hoc feature attribution, a family of methods for giving each feature in an input a score corresponding to its influence on a model's output. A major limitation of this family of explainers in practice is that they can disagree on which features are more important than others. Our contribution in this paper is a method of training models with this disagreement problem in mind. We do this by introducing a Post hoc Explainer Agreement Regularization (PEAR) loss term alongside the standard term corresponding to accuracy, an additional term that measures the difference in feature attribution between a pair of explainers. We observe on three datasets that we can train a model with this loss term to improve explanation consensus on unseen data, and see improved consensus between explainers other than those used in the loss term. We examine the trade-off between improved consensus and model performance. And finally, we study the influence our method has on feature attribution explanations.
Abstract (translated)
神经网络在高风险环境中越来越频繁地做出关键决策，因此理解和可信地监测和解释其行为是至关重要的。一种常见的解释器类型是后处理特征归因，一种方法 family，为给输入每个特征赋予其对模型输出的影响对应的得分而提供一组方法。在实践中，这个 family 的主要限制是它们可能不同意哪个特征比其他特征更重要。本文的贡献是考虑这个问题并使用一种后处理解释器协议 regularization (PEAR) loss Term，与精度标准 term 一起使用，并添加一个用于衡量两个解释器之间的特征归因差异的新 term。我们在三个数据集上观察，可以训练模型使用这个 loss Term 来提高 unseen 数据下的解释一致性，并观察除了用于 loss Term 的解释器之外之间的更好的一致性。我们研究提高一致性和模型性能之间的权衡。最后，我们研究我们的方法对特征归因解释的影响。
URL
https://arxiv.org/abs/2303.13299
