合规卡：复杂AI供应链下的自动化欧盟AI法案合规分析
Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain
AI总结：
针对复杂AI供应链下欧盟AI法案合规分析流程复杂耗时的问题，提出含多利益相关方透明度工件与合规预测算法的自动化系统，可实时完成提供商侧合规分析，推动合规普及。
摘要：
随着AI供应链日趋复杂，AI系统与模型越来越多地整合多个内部或外部来源的组件，例如数据集与（预训练）模型。在此类情况下，判断聚合后的AI系统或模型是否符合《欧盟AI法案》（AIA）要求，需要一个多步骤流程：针对AI系统/模型及其所有组成部分的合规相关信息，需（1）从多个独立来源收集；（2）必要时进行协调统一；（3）输入到跨全部信息的分析流程中，以生成合规预测。由于该流程极为复杂耗时，可能会让承担主要AIA合规责任的AI提供商（即开发者）有限的合规资源不堪重负，同时也导致在许多对提供商有益的AI开发场景中，无法开展快速或实时合规分析。为解决这些不足，我们提出了一套完整系统，用于在复杂AI供应链下实现提供商侧的AIA合规分析自动化。该系统包含两大核心要素：一是一套相互关联的计算型多利益相关方透明度工件，可捕获两类AIA特定元数据：（1）提供商的整体AI系统或模型的元数据；（2）其作为组件整合的数据集与预训练模型的元数据。二是一种可跨所有上述工件运行的算法，能实时预测聚合后的AI系统或模型是否符合AIA要求。总体而言，该系统有望极大地推动并普及提供商侧的AIA合规分析（甚至可能进一步推动提供商侧的AIA合规落地）。
英文摘要：
As the AI supply chain grows more complex, AI systems and models are increasingly likely to incorporate multiple internally- or externally-sourced components such as datasets and (pre-trained) models. In such cases, determining whether or not the aggregate AI system or model complies with the EU AI Act (AIA) requires a multi-step process in which compliance-related information about both the AI system or model and all its component parts is: (1) gathered, potentially from multiple arms-length sources; (2) harmonized, if necessary; (3) inputted into an analysis that looks across all of it to render a compliance prediction. Because this process is so complex and time-consuming, it threatens to overburden the limited compliance resources of the AI providers (i.e., developers) who bear much of the responsibility for complying with the AIA. It also renders rapid or real-time compliance analyses infeasible in many AI development scenarios where they would be beneficial to providers. To address these shortcomings, we introduce a complete system for automating provider-side AIA compliance analyses amidst a complex AI supply chain. This system has two key elements. First is an interlocking set of computational, multi-stakeholder transparency artifacts that capture AIA-specific metadata about both: (1) the provider's overall AI system or model; and (2) the datasets and pre-trained models it incorporates as components. Second is an algorithm that operates across all those artifacts to render a real-time prediction about whether or not the aggregate AI system or model complies with the AIA. All told, this system promises to dramatically facilitate and democratize provider-side AIA compliance analyses (and, perhaps by extension, provider-side AIA compliance).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
