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.
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Notes
- 1.
Schengen Information System (SIS), Visa Information System (VIS), Entry-Exit System (EES), Eurodac, Europol data and Interpol SLTD and TDAWN.
- 2.
The ETIAS screening rules are planned to be used in future also when examining the risk of visa applicants (VIS Reg [84], Art. 9j), who will also be checked against the ETIAS watchlist (ibid., Art. 9a and 22b).
- 3.
See Sect. 3.2.
- 4.
The most prevalent approach to ML, supervised learning, refers to training models with labelled data, i.e. the training data consists of inputs and desired outputs [51].
- 5.
In this context see Art. 10 (3), Rec. 44 AI Act Proposal [28] that require data sets to be, among other things, sufficiently representative, accurate and complete.
- 6.
Schengen Information System (SIS), Visa Information System (VIS), Entry-Exit System (EES), Eurodac, Europol data and Interpol SLTD and TDAWN.
- 7.
See Sect. 2.
- 8.
For example, the Commission AI report refers to misuse and bias in the context of EU policy making and enforcement process [27].
- 9.
The provision refers to the offences referred to in Art. 2 (1) and 2 (2) of the Framework decision 2002/584/JHA [13].
- 10.
‘[D]ata recorded in Europol data’.
- 11.
- 12.
On the impossibility to travel for medical purposes due to a UN blacklist.
- 13.
According to the ECtHR, professional relations can be considered private life under Art. 8 ECHR [31] (on an impossibility to travel for medical and business purposes due to an entry ban in the SIS).
- 14.
See para 126 and the case-law cited there.
- 15.
On Art. 47 CFREU [11].
- 16.
The provision refers to the predecessor Regulation (EC) No 45/2001 that has been replaced by the EUDPR [82].
- 17.
For information and access rights see Sect. 5.3 with fn. 28, 31.
- 18.
Specifically, the AI Act Proposal [28] statutes an exemption for AI systems which are components of the large-scale IT systems established by the legal acts listed in Annex IX that have been placed on the market or put into service before 12 months after the date of application of the proposed AI Act, Art. 83 (1). The AI Act shall apply after 24 months following its entry into force, Art. 85 (2). Annex IX lists union legislations on large-scale IT systems in the area of freedom, security and justice, particularly the regulations concerning ETIAS (Nr. 5) that is expected to be operational by end of 2022 and hence is highly likely to fall under the exemption according to the AI Act Proposal [28]. However, the AI Act Proposal [28] stipulates that the requirements of the proposed regulation shall be taken into account in the evaluation of the exempted AI systems, Art. 83 (1). ETIAS will be evaluated three years after the start of operations and every four years thereafter, Art. 92 (5) ETIAS Regulation [81].
- 19.
Cf. AI Act Proposal [28], Art. 3 (1), Annex I (a).
- 20.
AI Act Proposal [28], p. 7. The proposal particularly classifies AI systems intended to be used for assessing risks posed by persons who intend to enter into the territory of a Member State (e.g. further ETIAS risk assessments in case of a hit), and AI systems intended to be used for making individual risk assessments regarding the risk of future criminal offences (e.g. upstream risk assessments which SIS or Europol entries are based on) as high-risk, Art. 6 (2), annex III Nr.6 (a), III Nr.7 (b) AI Act Proposal [28].
- 21.
- 22.
The LED provision explicitly covers ‘adverse’ legal effects only, Art. 11 (1) LED [24].
- 23.
The Art. 29 Working Party, WP251rev.01, p. 21 gives the ‘refused admission to a country’ as an example for such a decision.
- 24.
In the context of American prosecution procedure law (‘totality-of-the-circumstances-analysis’).
- 25.
See Sect. 3.1.
- 26.
See Sect. 5.1 on the applicability.
- 27.
On accuracy and non-discrimination see Sect. 5.4.
- 28.
No such explicit requirement exists under the LED, which demonstrates that the level of transparency provided for under the LED is lower as compared to the GDPR. This inconsistency could pose a problem where the determination of the applicable provisions is unclear (see Sect. 5.1).
- 29.
See Sect. 5.2.
- 30.
See Sect. 4.
- 31.
This requirement is not explicitly anchored in the LED [24].
- 32.
See Sect. 5.2.
- 33.
In the context of visa decisions.
- 34.
- 35.
From a statistical point of view, discrimination is the objective of risk assessments. See, for example, on the discriminatory power of credit scorings [5].
- 36.
See Sect. 3.1.
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Acknowledgements
The authors would like to thank Tobias Kupek, Rainer Böhme and our anonymous reviewers for valuable remarks.
Franziska Boehm and Paulina Jo Pesch participate in the project INDIGO (Information in the EU’s Digitalized Governance). The project is financially supported by the NORFACE Joint Research Programme on Democratic Governance in a Turbulent Age and co-funded by AEI, AKA, DFG and FNR, and the European Commission through Horizon 2020 under grant agreement No. 822166.
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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: Gryszczyńska, A., Polański, P., Gruschka, N., Rannenberg, K., Adamczyk, M. (eds) Privacy Technologies and Policy. APF 2022. Lecture Notes in Computer Science(), vol 13279. Springer, Cham. https://doi.org/10.1007/978-3-031-07315-1_4
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