A4 Refereed article in a conference publication
Predicting the Amount of GDPR Fines
Authors: Ruohonen Jukka, Hjerppe Kalle
Editors: Andrea Tagarelli, Ester Zumpano, Aida Kamisalić Latifić, Andrea Calì
Conference name: COUrT - CAiSE for Legal Documents
Publication year: 2020
Journal: CEUR Workshop Proceedings
Book title : Proceedings of the First International Workshop “CAiSE for Legal Documents” (COUrT 2020)
Series title: CEUR Workshop Proceedings
Volume: 2690
First page : 3
Last page: 14
ISSN: 1613-0073
Web address : http://ceur-ws.org/Vol-2690/COUrT-paper1.pdf
Self-archived copy’s web address: https://arxiv.org/abs/2003.05151
Abstract. The General Data Protection Regulation (GDPR) was enforced in 2018. After this enforcement, many fines have already been
imposed by national data protection authorities in the European Union
(EU). This paper examines the individual GDPR articles referenced in
the enforcement decisions, as well as predicts the amount of enforcement
fines with available meta-data and text mining features extracted from
the enforcement decision documents. According to the results, articles
related to the general principles, lawfulness, and information security
have been the most frequently referenced ones. Although the amount of
fines imposed vary across the articles referenced, these three particular
articles do not stand out. Furthermore, good predictions are attainable
even with simple machine learning techniques for regression analysis. Basic meta-data (such as the articles referenced and the country of origin)
yields slightly better performance compared to the text mining features.
Keywords: Text mining · Legal mining · Data protection · Law enforcement
Downloadable publication This is an electronic reprint of the original article. |  |
