Towards Interrogating Discriminative Machine Learning Models

TL;DR

This paper introduces a novel approach combining Bayesian regression mixtures with elastic nets to provide comprehensive explanations of machine learning models, improving interpretability and vulnerability detection across tasks.

Contribution

It proposes a new method that enhances model interpretability by globally approximating models and identifying vulnerabilities, surpassing existing explanation techniques.

Findings

Outperforms state-of-the-art in explaining individual decisions

Enables discovery of model vulnerabilities

Effective across text and image tasks

Abstract

It is oftentimes impossible to understand how machine learning models reach a decision. While recent research has proposed various technical approaches to provide some clues as to how a learning model makes individual decisions, they cannot provide users with ability to inspect a learning model as a complete entity. In this work, we propose a new technical approach that augments a Bayesian regression mixture model with multiple elastic nets. Using the enhanced mixture model, we extract explanations for a target model through global approximation. To demonstrate the utility of our approach, we evaluate it on different learning models covering the tasks of text mining and image recognition. Our results indicate that the proposed approach not only outperforms the state-of-the-art technique in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of a learning model.