为符合欧盟AI法案，提出GNN合规设计方法
Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Act
- 针对图神经网络设计合规策略
- 解决数据管理与隐私等核心要求
- 适合关注AI监管的GNN研究者
欧盟人工智能法案（AI Act）为人工智能与机器学习系统的发展与监管提供了全面指导，对图神经网络（GNNs）产生重大影响。本文探讨了该法案对基于复杂图结构数据的GNN所带来独特挑战，涵盖数据管理、数据治理、鲁棒性、人工监督和隐私等方面的要求。研究分析了这些要求对GNN训练的影响，并提出确保合规的方法。重点考察了偏见、鲁棒性、可解释性和隐私问题，强调公平采样策略与有效可解释性技术的必要性。本工作填补了GNN在新立法框架下的研究空白，提供具体指导，并指出开放问题与未来研究方向。
原文摘要 · Abstract (English)
The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive guidelines for the development and oversight of Artificial Intelligence (AI) and Machine Learning (ML) systems, with significant implications for Graph Neural Networks (GNNs). This paper addresses the unique challenges posed by the AI Act for GNNs, which operate on complex graph-structured data. The legislation's requirements for data management, data governance, robustness, human oversight, and privacy necessitate tailored strategies for GNNs. Our study explores the impact of these requirements on GNN training and proposes methods to ensure compliance. We provide an in-depth analysis of bias, robustness, explainability, and privacy in the context of GNNs, highlighting the need for fair sampling strategies and effective interpretability techniques. Our contributions fill the research gap by offering specific guidance for GNNs under the new legislative framework and identifying open questions and future research directions.
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