Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation

TL;DR

This paper reviews privacy-preserving AI techniques in dataspaces, introduces a taxonomy for classifying these methods, analyzes key performance metrics, and outlines future research directions for compliant and trustworthy AI systems.

Contribution

It proposes a novel taxonomy for privacy-aware AI techniques in dataspaces and offers a comprehensive framework for balancing privacy, performance, and compliance.

Findings

Identified key performance metrics: latency, throughput, cost, utility, fairness, explainability.

Highlighted research gaps: lack of standardized KPIs, explainability challenges, regulatory fragmentation.

Outlined future directions: automated compliance, benchmarking, integration with European initiatives.

Abstract

As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and policy-aware AI techniques, including Federated Learning, Differential Privacy, Trusted Execution Environments, Homomorphic Encryption, and Secure Multi-Party Computation, alongside strategies for aligning AI with regulatory frameworks such as GDPR and the EU AI Act. We propose a novel taxonomy to classify these techniques based on privacy levels, performance impacts, and compliance complexity, offering a clear framework for practitioners and researchers to navigate trade-offs. Key performance metrics -- latency, throughput, cost overhead, model utility, fairness, and explainability -- are analyzed to highlight the multi-dimensional optimization required in dataspaces. The paper identifies critical research gaps, including the lack of standardized privacy-performance KPIs, challenges in explainable AI for federated ecosystems, and semantic policy enforcement amidst regulatory fragmentation. Future directions are outlined, proposing a conceptual framework for policy-driven alignment, automated compliance validation, standardized benchmarking, and integration with European initiatives like GAIA-X, IDS, and Eclipse EDC. By synthesizing technical, ethical, and regulatory perspectives, this work lays the groundwork for developing trustworthy, efficient, and compliant AI systems in dataspaces, fostering innovation in secure and responsible data-driven ecosystems.