Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution

TL;DR

This paper introduces a novel probabilistic framework for anomaly attribution in black-box regression models, enabling uncertainty quantification of input variable attributions without requiring access to training data.

Contribution

It proposes a generative perturbation-based method and a variational Bayes algorithm to compute both attribution scores and their uncertainties, addressing limitations of existing deviation-agnostic methods.

Findings

First probabilistic anomaly attribution framework.

Quantifies uncertainty in attribution scores.

Effective in black-box regression scenarios.

Abstract

We address the task of probabilistic anomaly attribution in the black-box regression setting, where the goal is to compute the probability distribution of the attribution score of each input variable, given an observed anomaly. The training dataset is assumed to be unavailable. This task differs from the standard XAI (explainable AI) scenario, since we wish to explain the anomalous deviation from a black-box prediction rather than the black-box model itself. We begin by showing that mainstream model-agnostic explanation methods, such as the Shapley values, are not suitable for this task because of their ``deviation-agnostic property.'' We then propose a novel framework for probabilistic anomaly attribution that allows us to not only compute attribution scores as the predictive mean but also quantify the uncertainty of those scores. This is done by considering a generative process for perturbations that counter-factually bring the observed anomalous observation back to normalcy. We introduce a variational Bayes algorithm for deriving the distributions of per variable attribution scores. To the best of our knowledge, this is the first probabilistic anomaly attribution framework that is free from being deviation-agnostic.