Privacy Implications of Explainable AI in Data-Driven Systems

TL;DR

This paper discusses the privacy challenges posed by explainable AI techniques in data-driven systems, highlighting the conflict between transparency and privacy preservation, and emphasizing the need for balanced solutions.

Contribution

It analyzes the privacy implications of XAI methods and explores the inherent conflict between explainability and privacy-preserving techniques in ML models.

Findings

XAI techniques can inadvertently expose sensitive model information.

Privacy-preserving methods like differential privacy may reduce explanation quality.

There is a critical need for balanced approaches to ensure both explainability and privacy.

Abstract

Machine learning (ML) models, demonstrably powerful, suffer from a lack of interpretability. The absence of transparency, often referred to as the black box nature of ML models, undermines trust and urges the need for efforts to enhance their explainability. Explainable AI (XAI) techniques address this challenge by providing frameworks and methods to explain the internal decision-making processes of these complex models. Techniques like Counterfactual Explanations (CF) and Feature Importance play a crucial role in achieving this goal. Furthermore, high-quality and diverse data remains the foundational element for robust and trustworthy ML applications. In many applications, the data used to train ML and XAI explainers contain sensitive information. In this context, numerous privacy-preserving techniques can be employed to safeguard sensitive information in the data, such as differential privacy. Subsequently, a conflict between XAI and privacy solutions emerges due to their opposing goals. Since XAI techniques provide reasoning for the model behavior, they reveal information relative to ML models, such as their decision boundaries, the values of features, or the gradients of deep learning models when explanations are exposed to a third entity. Attackers can initiate privacy breaching attacks using these explanations, to perform model extraction, inference, and membership attacks. This dilemma underscores the challenge of finding the right equilibrium between understanding ML decision-making and safeguarding privacy.