A4 Article in conference proceedings

Predicting the Amount of GDPR Fines




List of Authors: Ruohonen Jukka, Hjerppe Kalle

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)

Title of series: CEUR Workshop Proceedings

Volume number: 2690

ISSN: 1613-0073

URL: http://ceur-ws.org/Vol-2690/COUrT-paper1.pdf


Abstract

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


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Last updated on 2021-24-06 at 11:08