Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property

TL;DR

This paper introduces a novel explanation method for black-box models that simultaneously estimates high- and low-level feature attributions by leveraging their nested structure and a new consistency property, improving explanation faithfulness and efficiency.

Contribution

It proposes a model-agnostic local explanation technique that exploits nested feature structures and introduces a consistency property to enhance attribution accuracy and coherence.

Findings

Accurately estimates high- and low-level feature attributions

Produces faithful and consistent explanations with fewer model queries

Effective in image and text classification tasks

Abstract

Techniques that explain the predictions of black-box machine learning models are crucial to make the models transparent, thereby increasing trust in AI systems. The input features to the models often have a nested structure that consists of high- and low-level features, and each high-level feature is decomposed into multiple low-level features. For such inputs, both high-level feature attributions (HiFAs) and low-level feature attributions (LoFAs) are important for better understanding the model's decision. In this paper, we propose a model-agnostic local explanation method that effectively exploits the nested structure of the input to estimate the two-level feature attributions simultaneously. A key idea of the proposed method is to introduce the consistency property that should exist between the HiFAs and LoFAs, thereby bridging the separate optimization problems for estimating them. Thanks to this consistency property, the proposed method can produce HiFAs and LoFAs that are both faithful to the black-box models and consistent with each other, using a smaller number of queries to the models. In experiments on image classification in multiple instance learning and text classification using language models, we demonstrate that the HiFAs and LoFAs estimated by the proposed method are accurate, faithful to the behaviors of the black-box models, and provide consistent explanations.