Attribution of Predictive Uncertainties in Classification Models

TL;DR

This paper introduces a new framework for attributing predictive uncertainties in classification models, combining path integrals, counterfactual explanations, and generative models to improve interpretability and reduce artefacts.

Contribution

It proposes a novel attribution method that outperforms existing approaches by integrating path integrals, counterfactual explanations, and generative models for better uncertainty interpretation.

Findings

Outperforms existing attribution methods in quantitative benchmarks

Produces attributions with fewer artefacts and noise

Effective across various datasets and complexity levels

Abstract

Predictive uncertainties in classification tasks are often a consequence of model inadequacy or insufficient training data. In popular applications, such as image processing, we are often required to scrutinise these uncertainties by meaningfully attributing them to input features. This helps to improve interpretability assessments. However, there exist few effective frameworks for this purpose. Vanilla forms of popular methods for the provision of saliency masks, such as SHAP or integrated gradients, adapt poorly to target measures of uncertainty. Thus, state-of-the-art tools instead proceed by creating counterfactual or adversarial feature vectors, and assign attributions by direct comparison to original images. In this paper, we present a novel framework that combines path integrals, counterfactual explanations and generative models, in order to procure attributions that contain few observable artefacts or noise. We evidence that this outperforms existing alternatives through quantitative evaluations with popular benchmarking methods and data sets of varying complexity.