  As the artificial intelligence (AI) supply chain grows more complex, AI
systems and models are increasingly likely to incorporate externally-sourced
ingredients such as datasets and other models. In such cases, determining
whether or not an AI system or model complies with the EU AI Act will require
gathering compliance-related metadata about both the AI system or model
at-large as well as those externally-supplied ingredients. There must then be
an analysis that looks across all of this metadata to render a prediction about
the compliance of the overall AI system or model. Up until now, this process
has not been automated. Thus, it has not been possible to make real-time
compliance determinations in scenarios where doing so would be advantageous,
such as the iterative workflows of today's AI developers, search and
acquisition of AI ingredients on communities like Hugging Face, federated and
continuous learning, and more. To address this shortcoming, we introduce a
highly automated system for AI Act compliance analysis. This system has two key
elements. First is an interlocking set of computational artifacts that capture
compliance-related metadata about both: (1) the AI system or model at-large;
(2) any constituent ingredients such as datasets and models. Second is an
automated analysis algorithm that operates across those computational artifacts
to render a run-time prediction about whether or not the overall AI system or
model complies with the AI Act. Working together, these elements promise to
enhance and accelerate AI Act compliance assessments.
