AI Training Datasets & Article 14 GDPR

A Risk Assessment for the Proportionality Exemption of the Obligation to Provide Information

Authors

DOI:

https://doi.org/10.26512/lstr.v13i2.36253

Keywords:

AI. GDPR. Article 14. Risk-Assessment. Transparency.

Abstract

[Purpose] At the earliest stages in AI lifecycle, training, verification and validation of machine learning and deep learning algorithm require vast datasets that usually contain personal data, which however is not obtained directly from the data subject, while very often the controller is not in a position to identify the data subjects or such identification may result to disproportionate effort. This situation raises the question on how the controller can comply with its obligation to provide information for the processing to the data subjects, especially when proving the information notice is impossible or requires a disproportionate effort. There is little to no guidance on the matter. The purpose of this paper is to address this gap by designing a clear risk-assessment methodology that can be followed by controllers when providing information to the data subjects is impossible or requires a disproportionate effort.

[Methodology] After examining the scope of the transparency principle, Article 14 and its proportionality exemption in the training and verification stage of machine learning and deep learning algorithms following a doctrinal analysis, we assess whether already existing tools and methodologies can be adapted to accommodate the GDPR requirement of carrying a balancing test, in conjunction with, or independently of a DPIA.

[Findings] Based on an interdisciplinary analysis, comprising theoretical and descriptive material from a legal and technological point of view, we propose a risk-assessment methodology as well as a series of risk-mitigating measures to ensure the protection of the data subject's rights and legitimate interests while fostering the uptake of the technology.

[Practical Implications] The proposed balancing exercise and additional measures are designed to facilitate entities training or developing AI, especially SMEs, within and outside of the EEA, that wish to ensure and showcase the data protection compliance of their AI-based solutions.

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Author Biographies

Iakovina Kindylidi, Vieira de Almeida & Associates

Iakovina Kindylidi is an international adviser at Vieira de Almeida & Associados’ ICT practice area. She holds an LL.M in International Business Law from Tilburg University and has participated as speaker in various seminars and classes on emerging technologies, with a focus on AI.

Inês Antas de Barros, Vieira de Almeida & Associados

Inês Antas de Barros is a managing associate at Vieira de Almeida & Associados’ ICT practice area. She holds an LL.M in International Business Law from Global School of Law of the Catholic University of Portugal. She has participated as a speaker in various seminars and classes on privacy, data protection, and cybersecurity.

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Published

2021-09-07

How to Cite

KINDYLIDI, Iakovina; ANTAS DE BARROS, Inês. AI Training Datasets & Article 14 GDPR: A Risk Assessment for the Proportionality Exemption of the Obligation to Provide Information. Law, State and Telecommunications Review, [S. l.], v. 13, n. 2, p. 1–27, 2021. DOI: 10.26512/lstr.v13i2.36253. Disponível em: https://periodicos.unb.br/index.php/RDET/article/view/36253. Acesso em: 28 mar. 2024.