Tough Decisions? Supporting System Classification According to the AI Act
Hilmy Hanif (Student TU Delft)
J.E. Constantino (TU Delft - Organisation & Governance)
M.T. Sekwenz (TU Delft - Organisation & Governance)
MJG van Eeten (TU Delft - Organisation & Governance)
J Ubacht (TU Delft - Information and Communication Technology)
Ben Wagner (TU Delft - Organisation & Governance)
Y. Zhauniarovich (TU Delft - Organisation & Governance)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The AI Act represents a significant legislative effort by the European Union to govern the use of AI systems according to different risk-related classes, linking varying degrees of compliance obligations to the system's classification. However, it is often critiqued due to the lack of general public comprehension and effectiveness regarding the classification of AI systems to the corresponding risk classes. To mitigate those shortcomings, we propose a Decision-Tree-based framework aimed at increasing robustness, legal compliance and classification clarity with the Regulation. Quantitative evaluation shows that our framework is especially useful to individuals without a legal background, allowing them to improve considerably the accuracy and significantly reduce the time of case classification.
help_outline