Skip to main navigation Skip to search Skip to main content

Challenges of Enforcing Regulations in Artificial Intelligence Act: Analyzing Quantity Requirement in Data and Data Governance

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
115 Downloads (Pure)

Abstract

To make Artificial Intelligence (AI) systems and services accountable and regulated in the European Union market, in April 2021, the European Union Parliament published a proposal `Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act)', widely known as Artificial Intelligence Act (AI Act). Since then, many concerns have been raised in terms of compliance and whether the regulations are enforceable. However, to the best of our knowledge, none of them provided an explicit technical analysis of the challenges in enforcing the regulation. Among 85 Articles in the AI Act, we emphasize on the Article 10, the central regulatory requirement for data and data governance. In this paper, we have analyzed a specific requirement, the data quantity, to show the challenges of enforcing this requirement in a principled way. In our analysis, we have used deep learning modeling and machine learning generalization theory.
Original languageEnglish
Title of host publicationProceedings of the 2022 1st International Workshop on Imagining the AI Landscape After the AI Act
Subtitle of host publicationIn conjunction with the first International Conference on Hybrid Human-Artificial Intelligence
PublisherCEUR-WS
Number of pages10
Volume3221
Publication statusPublished - 13 Jun 2022

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
ISSN (Print)1613-0073

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Trustworthy Artificial Intelligence
  • Artificial Intelligence Act
  • Future Technologies
  • Deep Learning Modeling
  • Generalization Theory

Cite this