The Right Not to Be Subject to Automated Individual Decision-Making/Profiling Concerning Big Health Data. Developing an Algorithmic Culture
15 Pages Posted: 2 Apr 2021
Date Written: March 11, 2021
Τhis paper explores the legal issues that arise from the collection and processing of Big Health Data in the light of the EU legislation on Data Protection, placing particular emphasis on the General Data Protection Regulation. Whether Big Health Data can be characterised as “personal data” or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Moreover, data subject’s rights, e.g., the right not to be subject to a decision based solely on automated processing, are heavily impacted by the use of AI, algorithms and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial source of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subjects’ rights while embracing the opportunities that Big Health Data have to offer.
Note: Funding Statement: This paper is based on the research project “Legal issues concerning the collection and processing of Big Health Data in view of the EU Regulation 679/2016 (General Data Protection Regulation)” (MIS 5047861) of Operational Program ‘Human Resources, Development, Education and Lifelong Learning’ – NSRF 2014-2020, which is co-financed by the ESF and national funds.
Declaration of Interests: The corresponding author confirms on behalf of all authors that there have been no involvements that might raise the question of bias in the work reported or in the conclusions, implications, or opinions stated.
Keywords: Big data, health data, genetic data, privacy, GDPR, processing principles, algorithm accountability, automated profiling
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