Data Protection and Machine-Learning-Supported Decision-Making at the EU Border: ETIAS Profiling Under Scrutiny
Jo Pesch P, Dimitrova D, Boehm F (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13279 LNCS
Pages Range: 50-72
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Warsaw, POL
ISBN: 9783031073144
DOI: 10.1007/978-3-031-07315-1_4
Abstract
ETIAS is an upcoming, largely automated IT system to identify risks posed by visa-exempt Third Country Nationals (TCNs) traveling to the Schengen area. It is expected to be operational by the end of 2022. The largely automated ETIAS risk assessments include the check of traveller data against not yet defined abstract risk indicators which might discriminate against certain groups of travellers. Moreover, there is evidence for the planned use of machine learning (ML) for risk assessments under the ETIAS framework. The risk assessments that could result in personal data being entered into terrorist watchlists or in a refusal of a travel authorisation have strong impacts especially on the fundamental right to data protection. The use of ML-trained models for such risk assessments raises concerns, since existing models lack transparency and, in some cases, have been found to be significantly biased. The paper discusses selected requirements under EU data protection law for ML-trained models, namely human oversight, information and access rights, accuracy, and supervision. The analysis considers provisions of the AI Act Proposal of the European Commission as the proposed regulation can provide guidance for the application of existing data protection requirements to AI.
Involved external institutions
Karlsruhe Institute of Technology (KIT)
Germany (DE)
FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur / Leibniz Institute for Information Infrastructure
Germany (DE)
Germany (DE)
FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur / Leibniz Institute for Information Infrastructure
Germany (DE)
How to cite
APA:
Jo Pesch, P., Dimitrova, D., & Boehm, F. (2022). Data Protection and Machine-Learning-Supported Decision-Making at the EU Border: ETIAS Profiling Under Scrutiny. In Agnieszka Gryszczyńska, Przemysław Polański, Nils Gruschka, Kai Rannenberg, Monika Adamczyk (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 50-72). Warsaw, POL: Springer Science and Business Media Deutschland GmbH.
MLA:
Jo Pesch, Paulina, Diana Dimitrova, and Franziska Boehm. "Data Protection and Machine-Learning-Supported Decision-Making at the EU Border: ETIAS Profiling Under Scrutiny." Proceedings of the 10th Annual Privacy Forum, APF 2022, Warsaw, POL Ed. Agnieszka Gryszczyńska, Przemysław Polański, Nils Gruschka, Kai Rannenberg, Monika Adamczyk, Springer Science and Business Media Deutschland GmbH, 2022. 50-72.
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