Navigating data governance risks: Facial recognition in law enforcement under EU legislation

Facial recognition technologies (FRTs) are used by law enforcement agencies (LEAs) for various purposes, including public security, as part of their legally mandated duty to serve the public interest. While these technologies can aid LEAs in fulfilling their public security responsibilities, they pose significant risks to data protection rights. This article identifies four specific risks associated with the use of FRT by LEAs for public security within the frameworks of the General Data Protection Regulation and Artificial Intelligence Act. These risks particularly concern compliance with fundamental data protection principles, namely data minimisation, purpose limitation, data and system accuracy, and administrative challenges. These challenges arise due to legal, technical, and practical factors in developing algorithms for law enforcement. Addressing these risks and exploring practical mitigations, such as broadening the scope of data protection impact assessments, may enhance transparency and ensure that FRT is used for public security in a manner that serves the public interest.

Authors

Publication Details

Journal
Internet Policy Review
Published
2024-09-30
DOI
https://doi.org/10.14763/2024.3.1798
Citations
6
Primary Topic
European Criminal Justice and Data Protection
Type
article
Field-Weighted Citation Impact
13.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Navigating data governance risks: Facial recognition in law enforcement under EU legislation

Gizem Gültekin-Várkonyi
6 citations
Internet Policy Review
European Criminal Justice and Data Protection
13.39
article

Navigating data governance risks: Facial recognition in law enforcement under EU legislation

Gizem Gültekin-Várkonyi
article en
6 citations

Abstract

Facial recognition technologies (FRTs) are used by law enforcement agencies (LEAs) for various purposes, including public security, as part of their legally mandated duty to serve the public interest. While these technologies can aid LEAs in fulfilling their public security responsibilities, they pose significant risks to data protection rights. This article identifies four specific risks associated with the use of FRT by LEAs for public security within the frameworks of the General Data Protection Regulation and Artificial Intelligence Act. These risks particularly concern compliance with fundamental data protection principles, namely data minimisation, purpose limitation, data and system accuracy, and administrative challenges. These challenges arise due to legal, technical, and practical factors in developing algorithms for law enforcement. Addressing these risks and exploring practical mitigations, such as broadening the scope of data protection impact assessments, may enhance transparency and ensure that FRT is used for public security in a manner that serves the public interest.

Internet Policy ReviewVol. 13(3)
Szegedi Tudományegyetem
Openalex Percentile: Top 1%
European Criminal Justice and Data Protection
13.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.