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

arXiv: https://arxiv.org/abs/2507.20014 · HTML: https://mlsanity.com/paper/2507.20014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 64 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 27 of 27)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices](https://mlsanity.com/paper/2411.12990/) (2024)
- [Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation](https://mlsanity.com/paper/2502.06559/) (2025)
- [Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network](https://mlsanity.com/paper/2106.07229/) (2021)
- [Consistency Models for Scalable and Fast Simulation-Based Inference](https://mlsanity.com/paper/2312.05440/) (2023)
- [Verification methods for international AI agreements](https://mlsanity.com/paper/2408.16074/) (2024)
- [Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels](https://mlsanity.com/paper/2305.10113/) (2023)
- [Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review](https://mlsanity.com/paper/2411.05874/) (2024)
- [A Comprehensive Survey on Federated Learning: Concept and Applications](https://mlsanity.com/paper/2201.09384/) (2022)
- [Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence](https://mlsanity.com/paper/2504.17703/) (2025)
- [Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach](https://mlsanity.com/paper/2401.02453/) (2024)
- [Verifiable Privacy-Preserving Computing](https://mlsanity.com/paper/2309.08248/) (2023)
- [Trusted Execution Environment for Decentralized Process Mining](https://mlsanity.com/paper/2312.12105/) (2023)
- [A Somewhat Homomorphic Encryption Scheme based on Multivariate Polynomial Evaluation](https://mlsanity.com/paper/1902.05771/) (2019)
- [Encrypted Vector Similarity Computations Using Partially Homomorphic Encryption: Applications and Performance Analysis](https://mlsanity.com/paper/2503.05850/) (2025)
- [Beyond Explainability: The Case for AI Validation](https://mlsanity.com/paper/2505.21570/) (2025)
- [Authentication and authorization in Data Spaces: A relationship-based access control approach for policy specification based on ODRL](https://mlsanity.com/paper/2505.24742/) (2025)
- [A Service Architecture for Dataspaces](https://mlsanity.com/paper/2507.07979/) (2025)
- [Efficient and Scalable Self-Healing Databases Using Meta-Learning and Dependency-Driven Recovery](https://mlsanity.com/paper/2507.13757/) (2025)

## Abstract (arXiv)

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.