Privacy Implications of Explainable AI in Data-Driven Systems

arXiv:2406.15789v1 Announce Type: cross
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 …

address arxiv black box box challenge cs.ai cs.cr cs.lg data data-driven explainability explainable ai machine machine learning ml models nature privacy systems techniques transparency trust xai