Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligence

XAI creates a privacy paradox: While explainable AI methods help make AI decision-making more transparent and trustworthy, they can paradoxically leak sensitive personal information about individuals whose data was used to train the models—a risk that currently lacks adequate defenses in real-world AI systems.

Privacy attacks on explanations are a concrete threat: Researchers have demonstrated that attackers can exploit the explanations provided by AI systems to infer sensitive personal attributes about individuals, meaning transparency itself can become a vulnerability if not properly protected.

Privacy-enhancing technologies offer partial solutions: The paper tested three approaches—synthetic training data, differential privacy, and noise addition—and found they can reduce privacy attack success by up to 49.47% while maintaining the usefulness of both the AI model and its explanations, though each method has different trade-offs.

There's no one-size-fits-all defense: Different privacy protection methods produce different results depending on the type of explanation method used, suggesting organizations need to carefully evaluate which combination of privacy and explainability tools works best for their specific AI applications.

Balancing transparency with privacy requires intentional design: Organizations deploying explainable AI systems should actively integrate privacy protections rather than treating transparency and privacy as separate concerns, as the paper identifies specific strategies for maximizing privacy benefits without sacrificing explanation quality.