Gloria González Fuster, warned against the existence of “false friends” among different branches of EU law, stating that the meaning of “accountability”, “transparency” and “fairness” in the GDPR is specific, and thus these terms cannot be uncritically applied as such in other sectors of law. What could indicate parallelisms and a certain complementarity might, in reality, hide a gap. She contested the possibility to reduce discriminatory processing to a violation of the “fairness” of processing, as the notion of unfairness in automatic decision making in the GDPR was not always related to discrimination. For example, in the case where personal data were not accurate, not properly collected or not meaningful, data processing could be in tension with fairness, but not necessarily discriminatory.
She admitted a relation between discrimination and the special categories of data under the GDPR, which are more especially protected in principle because they relate to grounds where there exists a heightened risk of discrimination. Nevertheless, the special categories of data under GDPR only vaguely correspond to those of Article 21 of the Charter on Fundamental Rights of the European Union (CFR), since whereas the Article 21 provides for an open clause of potentially discriminatory grounds, the GDPR has a closed list, that does not encompass for instance gender-based discrimination. In the end, she highlighted the importance of preventing discrimination rather than adopting actions a posteriori.
Finally, the author was given the opportunity to respond to comments received. Regarding the comment that data protection concepts and anti-discrimination law concepts might not easily be interchangeable, Hacker reiterated that there was an opportunity to use concepts belonging to other branches of law if there are sufficient links between them. For fairness, this was supported by the Article 29 Working Party (WP29), who also suggested that there is relation between fairness in data protection law and discrimination.
Hacker then urged companies to be more proactive and to involve people affected by technology in the testing phase. He warned against the risks of gaps under EU data protection law, whereas anonymous training data of algorithms would fall outside the scope of the GDPR. He suggested the creation of legal mechanisms to ensure that training data are up to date, to put responsibility on the companies.
A lively Q&A session with the audience followed. The attendees expressed their concern about the difficulties in understanding the logic involved in algorithmic decision-making and proposed to involve NGOs in the auditing of automated decisions. Standardization could also play an important role, it was argued. Forms of civil liability in case of violations could be introduced, but due to the variety of parties involved in the process, an approach similar to product liability should apply, it was mentioned. The ideas to strengthen cooperation between data protection and equality regulators or, even further, to create specialized bodies in charge of dealing with algorithm discrimination composed by data protection and equality specialists were also raised.
Hijmans concluded the debate expressing his appreciation on the different ideas raised and inviting the audience to the next Meet the Author event subject and date to be communicated.
1 Hacker, Philipp, Teaching Fairness to Artificial Intelligence: Existing and Novel Strategies Against Algorithmic Discrimination Under EU Law, Common Market Law Review 55, pp. 1143–1186, 2018
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