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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2024
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| Accesso online: | https://arxiv.org/abs/2406.14758 |
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| _version_ | 1866916391571947520 |
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| author | Marino, Bill Chaudhary, Yaqub Pi, Yulu Yew, Rui-Jie Aleksandrov, Preslav Rahman, Carwyn Shen, William F. Robinson, Isaac Lane, Nicholas D. |
| author_facet | Marino, Bill Chaudhary, Yaqub Pi, Yulu Yew, Rui-Jie Aleksandrov, Preslav Rahman, Carwyn Shen, William F. Robinson, Isaac Lane, Nicholas D. |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain Marino, Bill Chaudhary, Yaqub Pi, Yulu Yew, Rui-Jie Aleksandrov, Preslav Rahman, Carwyn Shen, William F. Robinson, Isaac Lane, Nicholas D. Artificial Intelligence 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). |
| title | Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.14758 |