Challenges and Legal Gaps of Genetic Profiling in the Era of Big Data

Abstract

Profiling of individuals based on inborn, acquired, and assigned characteristics is central for decision making in health care. In the era of omics and big smart data, it becomes urgent to differentiate between different data governance affordances for different profiling activities. Typically, diagnostic profiling is in the focus of researchers and physicians, and other types are regarded as undesired side-effects; for example, in the connection of health care insurance risk calculations. Profiling in a legal sense is addressed, for example, by the EU data protection law. It is defined in the General Data Protection Regulation as automated decision making. This term does not correspond fully with profiling in biomedical research and healthcare, and the impact on privacy has hardly ever been examined. But profiling is also an issue concerning the fundamental right of non-discrimination, whenever profiles are used in a way that has a discriminatory effect on individuals. Here, we will focus on genetic profiling, define related notions as legal and subject-matter definitions frequently differ, and discuss the ethical and legal challenges.

The ethical and legal landscape of brain data governance

“…Some sources suggest that with the advent of machine learning algorithms it is still possible that not all potential discriminatory effects will be reasonably foreseeable to allow pre-emptive action due to the way such algorithm works [A 47 ]. Sources also state that it is also possible that these non-foreseeable discriminatory effects are based on cultural assumptions and it is known that cultural assumptions are sometimes considered to be statistically invalid and somewhat based on ideology and can therefore be discriminating [A 54 ]. Furthermore reference to anti-discrimination shows that laws have often been considered to be ineffective due to the fact that it can only be applied when it can be proved that a decision was made on discriminatory presumptions which is usually difficult [A 54 ].…”

“…Some of the sources describe transparency as the availability of information about an actor that allows other actors to monitors the working performance of such actor [A 46 ] while some describe transparency in the context of governance as the accessibility and visibility of the governance structures of consortia [A 107 ]. Sources also referred to transparency as openness [A 42 , 107 , 108 ] References to transparency include using community engagement activities to promote trustworthiness and visibility [ 79 , 106 , 108 ] and using transparency to strengthen public confidence in institutions or research projects which ensures accountability while facilitating public trust [A 41 , 46 , 48 , 54 , 79 , 108 ]. This can include sharing information about the proposed use of data, expected societal benefits, harm-minimisation strategies, degree of security and encryption and research results [A 16 , 108 ].…”

Anticipatory Governance in Biobanking: Security and Risk Management in Digital Health

“…Emphasised in this regard are mostly the risks of illegitimate access (by unauthorised individuals) to big-data and the misuse of those data. However, others also raise concerns regarding the secondary use of health data (O' Doherty et al, 2016), such as the availability of biobanking data to governmental agencies, their use for other than health research purposes, or their integration with other type of data about participants and others (Sankar & Parker, 2017;Sariyar & Schlünder, 2019). How are these concerns reflected among biobanking professionals in Europe, and how do they anticipate future insecurities that this technology may bring about?…”

“…It thus contains three elements: "it has to be an automated form of processing; it has to be carried out on personal data; and the objective of the profiling must be to evaluate personal aspects about a natural person" (European Commission, 2018: 6-7). While "[g]enetic profiling is used in healthcare and biomedical research for associating genetic characteristics with increased or decreased likelihood of developing and overcoming certain diseases" (Sariyar & Schlünder, 2019: 3), medical profiling can be understood as "new services offering direct-to-consumer body imaging as a health check and personal genetic profiling for individual susceptibility to disease" (Nuffield Council on Bioethics, 2010: xvii).…”

“…Although the security of big data is discussed in diverse fields, discussion of biobanking as a specific area of bigdata research is minimal. Debates on risk assessment and disaster recovery focus on mitigating risks (e.g., fires and flooding) to individual biobanks (Henderson et al, 2013;Simeon-Dubach et al, 2013), but discussions about societal risks related to the rise of organized collections of big medical data are surprisingly rare (Müller et al, 2020;O'Doherty et al, 2016;Sankar & Parker, 2017;Sariyar & Schlünder, 2019). At the same time, we can observe raising interest of states in the use of big medical data in political governance-not only for digital epidemiology, but also for policing and surveillance (Wee, 2020).…”