The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/bf7013ce-54b5-43b9-b275-f6c44652534b

Hanif, Hilmy; Constantino, Jorge; Sekwenz, Marie-Therese; van Eeten, Michel; Ubacht, Jolien et. al. (2024): Navigating the EU AI Act Maze using a Decision-Tree Approach: Decision Tree and Interview Protocol. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/bf7013ce-54b5-43b9-b275-f6c44652534b.v1

Dataset

• Law and Legal Studies

AI Act, AIA, Artificial Intelligence, Compliance, Risk Classification

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Hilmy Hanif , Jorge Constantino , Marie-Therese Sekwenz , Michel van Eeten , Jolien Ubacht , Ben Wagner , Yury Zhauniarovich

This repository contains the Decision-Tree-based framework (AIAct_DecisionTree.pdf) and Interview protocol (InterviewProtocol.pdf) artifacts. The framework has been developed to improve legal compliance and classification clarity of AI systems in accordance with the AI Act. The protocol describes the interview process that has been used to collect the data in order to evaluate the efficiency and effectiveness of the developed framework.

• 2024-05-06 first online, published, posted

4TU.ResearchData

*.pdf

TU Delft, Faculty of Technology, Policy and Management