AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act
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ID: 283234
2024
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Abstract
With the upcoming enforcement of the EU AI Act, documentation of high-risk AI
systems and their risk management information will become a legal requirement
playing a pivotal role in demonstration of compliance. Despite its importance,
there is a lack of standards and guidelines to assist with drawing up AI and
risk documentation aligned with the AI Act. This paper aims to address this gap
by providing an in-depth analysis of the AI Act's provisions regarding
technical documentation, wherein we particularly focus on AI risk management.
On the basis of this analysis, we propose AI Cards as a novel holistic
framework for representing a given intended use of an AI system by encompassing
information regarding technical specifications, context of use, and risk
management, both in human- and machine-readable formats. While the
human-readable representation of AI Cards provides AI stakeholders with a
transparent and comprehensible overview of the AI use case, its
machine-readable specification leverages on state of the art Semantic Web
technologies to embody the interoperability needed for exchanging documentation
within the AI value chain. This brings the flexibility required for reflecting
changes applied to the AI system and its context, provides the scalability
needed to accommodate potential amendments to legal requirements, and enables
development of automated tools to assist with legal compliance and conformity
assessment tasks. To solidify the benefits, we provide an exemplar AI Card for
an AI-based student proctoring system and further discuss its potential
applications within and beyond the context of the AI Act.
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| Authors | Delaram Golpayegani; Isabelle Hupont; Cecilia Panigutti; Harshvardhan J. Pandit; Sven Schade; Declan O'Sullivan; Dave Lewis |
| Journal | arXiv |
| Year | 2024 |
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