The GDPR Enforcement Fines at Glance

• URL: http://arxiv.org/abs/2011.00946v3
• Date: Wed, 1 Sep 2021 09:13:46 GMT
• Title: The GDPR Enforcement Fines at Glance
• Abstract summary: General Data Protection Regulation came into force in European Union (EU) in 2018. Many fines have been imposed by national protection authorities in Europe already. This paper examines individual articles referenced in, as well as predicts amount of fines with available metadata mining features extracted from enforcement documents.
• Score: 0.7106986689736826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The General Data Protection Regulation (GDPR) came into force in 2018. After this enforcement, many fines have already been imposed by national data protection authorities in Europe. 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, three 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, a better statistical evidence is available with other meta-data features, including information about the particular European countries in which the enforcements were made. Accurate predictions are attainable even with simple machine learning techniques for regression analysis. Basic text mining features outperform the meta-data features in this regard. In addition to these results, the paper reflects the GDPR's enforcement against public administration obstacles in the European Union (EU), as well as discusses the use of automatic decision-making systems in judiciary.

Related papers

• The European Union general data protection regulation: what it is and what it means [0.17041248235270653]
  paper introduces strategic approach regulating data and the normative foundations' of European Union's General Data Protection Regulation ('General Data Regulation')<n>Paper explains genesis, as best understood an extension and complicate existing requirements imposed by 1995 Protection Directive; describe Data Data approach; make predictions about provisions; highlight U.S. privacy law implications.
  arXiv  Detail & Related papers  (2025-10-03T09:54:30Z)
• AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction [56.797874973414636]
  AnnoCaseLaw is a first-of-its-kind dataset of 471 meticulously annotated U.S. Appeals Court negligence cases. Our dataset lays the groundwork for more human-aligned, explainable Legal Judgment Prediction models. Results demonstrate that LJP remains a formidable task, with application of legal precedent proving particularly difficult.
  arXiv  Detail & Related papers  (2025-02-28T19:14:48Z)
• Machine Learning-Driven Convergence Analysis in Multijurisdictional Compliance Using BERT and K-Means Clustering [0.0]
  This study compares various regulations to identify areas where they overlap or diverge. International companies can learn valuable lessons from this report. The study's objective is to "bridge the gap between legal knowledge and technical expertise"
  arXiv  Detail & Related papers  (2025-01-23T22:11:18Z)
• An Analysis of European Data and AI Regulations for Automotive Organizations [0.0]
  This report summarizes the European Union's series of data and AI regulations and analyzes them for managers in automotive vehicle manufacturing organizations. In particular, we highlight the relevant ideas of the regulations, including how they find their roots in earlier legislation. Overall, we characterize European Union data regulations as a wave set, rooted in historical precedent, with important implications for the automotive industry.
  arXiv  Detail & Related papers  (2024-07-15T22:38:37Z)
• Rethinking Legal Compliance Automation: Opportunities with Large Language Models [2.9088208525097365]
  We argue that the examination of (textual) legal artifacts should, first employ broader context than sentences. We present a compliance analysis approach designed to address these limitations.
  arXiv  Detail & Related papers  (2024-04-22T17:10:27Z)
• Towards an Enforceable GDPR Specification [49.1574468325115]
  Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's. One emerging technique to realize PbD is enforcement (RE) We present a set of requirements and an iterative methodology for creating formal specifications of legal provisions.
  arXiv  Detail & Related papers  (2024-02-27T09:38:51Z)
• The Impact of Differential Feature Under-reporting on Algorithmic Fairness [86.275300739926]
  We present an analytically tractable model of differential feature under-reporting. We then use to characterize the impact of this kind of data bias on algorithmic fairness. Our results show that, in real world data settings, under-reporting typically leads to increasing disparities.
  arXiv  Detail & Related papers  (2024-01-16T19:16:22Z)
• SoK: The Gap Between Data Rights Ideals and Reality [46.14715472341707]
  Do rights-based privacy laws effectively empower individuals over their data? This paper scrutinizes these approaches by reviewing empirical studies, news articles, and blog posts.
  arXiv  Detail & Related papers  (2023-12-03T21:52:51Z)
• Entity Graph Extraction from Legal Acts -- a Prototype for a Use Case in Policy Design Analysis [52.77024349608834]
  This paper presents a prototype developed to serve the quantitative study of public policy design. Our system aims to automate the process of gathering legal documents, annotating them with Institutional Grammar, and using hypergraphs to analyse inter-relations between crucial entities.
  arXiv  Detail & Related papers  (2022-09-02T10:57:47Z)
• A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges [73.34944216896837]
  Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to predict judgment results based on fact descriptions automatically. We analyze 31 LJP datasets in 6 languages, present their construction process and define a classification method of LJP. We show the state-of-art results for 8 representative datasets from different court cases and discuss the open challenges.
  arXiv  Detail & Related papers  (2022-04-11T04:06:28Z)
• Post-processing of Differentially Private Data: A Fairness Perspective [53.29035917495491]
  This paper shows that post-processing causes disparate impacts on individuals or groups. It analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions. It proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics.
  arXiv  Detail & Related papers  (2022-01-24T02:45:03Z)
• Towards Automatic Comparison of Data Privacy Documents: A Preliminary Experiment on GDPR-like Laws [1.3537117504260623]
  General Data Protection Regulation (NLP) becomes standard law for protection in many countries. 12 countries adopt their similarities-like regulations, but evaluating differences is time-consuming and needs manual effort from legal experts. In this paper, we investigate a simple natural language processing (NLP) approach to tackle the problem.
  arXiv  Detail & Related papers  (2021-05-21T03:59:29Z)
• Data Protection Impact Assessment for the Corona App [0.0]
  SARS-CoV-2 started spreading in Europe in early 2020 and there has been a strong call for technical solutions to combat or contain the pandemic. There has been a strong call for technical solutions with contact tracing apps at the heart of debates. The EU's General Daten Protection Regulation (DPIA) requires controllers to carry out a data protection assessment. We present a scientific DPIA which thoroughly examines three published contact tracing app designs that are considered to be the most "privacy-friendly"
  arXiv  Detail & Related papers  (2021-01-18T19:23:30Z)
• Predicting the Amount of GDPR Fines [0.7106986689736826]
  This paper examines individual articles referenced in decisions as well as predicts amount of fines with available metadata text mining features extracted from enforcement documents. Basic meta-data (such as the articles referenced and country origin) yields slightly better performance compared to enforcement features.
  arXiv  Detail & Related papers  (2020-03-11T08:05:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.