Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain

• URL: http://arxiv.org/abs/2406.14758v2
• Date: Thu, 12 Sep 2024 20:19:38 GMT
• Title: Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain
• Authors: Bill Marino, Yaqub Chaudhary, Yulu Pi, Rui-Jie Yew, Preslav Aleksandrov, Carwyn Rahman, William F. Shen, Isaac Robinson, Nicholas D. Lane,
• Abstract summary: We introduce a complete system for provider-side AIA compliance analyses amidst a complex AI supply chain. First is an interlocking set of computational, multi-stakeholder transparency artifacts that capture AIA-specific metadata about both. 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.
• Score: 9.991293429067065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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