Learning Deep Attribution Priors Based On Prior Knowledge

TL;DR

This paper introduces the Deep Attribution Prior (DAPr) framework, which leverages prior knowledge about feature importance to improve the interpretability and generalization of deep learning models.

Contribution

The paper proposes a novel framework that integrates prior knowledge into feature attribution, enhancing explanation quality and model performance.

Findings

Improved model generalization to out-of-sample data.

Enhanced interpretability of model explanations.

Effective use of prior information for feature importance.

Abstract

Feature attribution methods, which explain an individual prediction made by a model as a sum of attributions for each input feature, are an essential tool for understanding the behavior of complex deep learning models. However, ensuring that models produce meaningful explanations, rather than ones that rely on noise, is not straightforward. Exacerbating this problem is the fact that attribution methods do not provide insight as to why features are assigned their attribution values, leading to explanations that are difficult to interpret. In real-world problems we often have sets of additional information for each feature that are predictive of that feature's importance to the task at hand. Here, we propose the deep attribution prior (DAPr) framework to exploit such information to overcome the limitations of attribution methods. Our framework jointly learns a relationship between prior information and feature importance, as well as biases models to have explanations that rely on features predicted to be important. We find that our framework both results in networks that generalize better to out of sample data and admits new methods for interpreting model behavior.