  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.
