Special Theme Issue
Guest Editors: Tamar Sharon* and Federica Lucivero**
* Interdisciplinary Hub for Security, Privacy and Data Governance, Radboud University, NL
** Ethox and Wellcome Centre for Ethics and Humanities, University of Oxford, UK
As in other domains, digital data are taking on an ever more central role in health and medicine today. And as it has in other domains, datafication is contributing to a re-configuration of health and medicine, prompting its expansion to include new spaces, new practices, new techniques and new actors. Indeed, possibilities to quantify areas of life that have not traditionally been considered the remit of biomedicine – such as a person’s consumption patterns, her social media activity or her dietary habits – have contributed to a redefinition of almost any data as health-related data (Lucivero and Prainsack, 2015; Weber et al., 2014). Increasingly, these data are being generated outside the traditional spaces of medicine, as people go about their daily lives interacting with consumer mobile devices. Similarly, the technological tools needed to capture, store, analyze and manage the flow of these data, from wearables and smart phones to cloud platforms and machine learning, increasingly rely on infrastructure and know-how that lie beyond the scope of traditional medical systems and scientists, amongst data scientists and ICT specialists. Moreover, new stakeholders are cropping up in these quasi-medical yet still undomesticated territories. On one end of the spectrum, individuals who generate health data as they track and monitor their health are both solicited as research participants and are making demands on researchers to utilize their personal health data (HDE, 2014). On the other end of the spectrum, consumer technology corporations such as Apple and Google are reinventing themselves as obligatory passage points for data-intensive precision medicine (Sharon, 2016). And somewhere in between, not-for-profit organizations, such as Sage Bionetworks and OpenHumans.org, are positioning themselves as mediators in this ecosystem in formation, between the medical research community, individual and collective generators of data, and technology developers.
As proponents uphold, this expansion and decentralization of the health data ecosystem is promising: it may advance data-driven research and healthcare, and it may render research more inclusive (Shen, 2015; Topol, 2015). But, as critical scholars of science and technology have consistently shown, a fuller grasp of our technological present must always include the far-reaching, unexpected and sometimes deleterious social, political and cultural effects of discourses of scientific progress and technologically-enabled democratization and participation. In recent years, such critical scholarship has been particularly wary of the new power asymmetries that datafication contributes to. Rather than levelling power relations, critics observe, these are being redrawn along new digital divides based on data ownership or access, control over digital infrastructures, and new types of computational expertise, where those who generate data, especially citizens, patients, and consumers, are positioned on the losing side of the on-going extraction and scramble for the world’s data driven by state and corporate actors (Andrejevic, 2014; boyd and Crawford, 2012; Taylor, 2017; Zuboff, 2016).
In the context of the data economy, the dominant response to these growing power differentials has been to ensure that individual data subjects acquire more control over the data they produce – what Prainsack calls the “Individual Control” approach in her contribution to this special theme. Examples include the EU’s General Data Protection Regulation or initiatives that allow individuals to monetize their personal data (Lanier, 2013; www.commodify.us). In the context of data-driven medicine, this emphasis on increasing individual control over data has translated into attempts to develop better anonymization techniques and more fine-grained informed consent (Kaye et al., 2015), as well as the configuration of patients as the rightful “owners” of their own medical data (Kish and Topol, 2015).
However, scholars from different disciplines have begun questioning whether enhancing individual control over data is the most effective or desirable means of addressing the new power differentials of digital society. While some scholars emphasize the relational and social nature of persons and data (Taylor, 2012), others question the legal feasibility of individual ownership of data (Evans, 2016), and others highlight the futility of monetization schemes as a means of redressing inequalities (Casilli, 2019). Most importantly, the emphasis on individual rights and values may result in a reframing of societal concerns as individual ones all the while undermining the political power of collectives.
Each of the contributions that make up this special theme addresses the reconfiguration of existing relationships and the emergence of new power differentials that result from the expansion of the health data ecosystem. While they do this from different disciplinary perspectives, they all share the same starting point: the understanding that increased individual control of data subjects is insufficient for anticipating the far-reaching risks and preventing the societal, if not individual, harms associated with this expansion. In light of this, they argue for new governance frameworks, technological infrastructures and narratives that are predicated on the shared responsibility of multiple stakeholders and collective decision-making and control.
The commentaries by Brian Bot, Lara Mangravite and John Wilbanks, and by Bart Jacobs and Jean Popma both discuss the types of technical methods and arrangements that need to be developed to enable secure, responsible and equitable data sharing in the context of decentralized medical research. Both groups of authors are involved in the design and implementation of novel data management infrastructures.
A better understanding of the workings of data management infrastructures is discussed in a filmed interview with José van Dijck, on the recent book she has co-authored with Thomas Poell and Martijn de Waal, Platform Society: Public Values in a Connective World (2018). Van Dijck and Sharon discuss the importance of grasping how the material functioning of internet platforms contributes to shaping a new political and social reality.
In their commentary, Alessandro Blasimme, Effy Vayena and Ine Van Hoyweghen scrutinize how the proliferation of citizen generation of medical data, in initiatives like the American “All of Us” program, is unsettling the position of a less commonly studied stakeholder: the private insurance sector. Such initiatives, they argue, create a new “information asymmetry” between private insurers and those of their policy-holders who enroll in such research, which will likely make people more reluctant to donate personal health data for precision medicine research.
Tuukka Lehtiniemi and Minna Ruckenstein focus on data activism as a means of challenging the power asymmetries of datafied societies. Based on their engagement as social scientists with MyData, a data activism initiative originating in Finland, they identify and disentangle two parallel social imaginaries, a “technological” and a “socio-cultural imaginary”. They discuss the benefits and disadvantages of each and call for a greater role for the latter, while acknowledging its weaknesses.
The contributions by Barbara Prainsack and Linnet Taylor & Nadezdha Purtova both address the limitations of the framework of the commons – today’s preferred site of theoretical and practical resistance for those scholars and activists seeking to counter digital power asymmetries by foregrounding collective, rather than individual, control over data. While Prainsack argues that a more systematic discussion of processes of inclusion and exclusion in commons is required, Taylor and Purtova call for more attention to which stakeholders are affected by data practices. Both agree that in light of the multiple nature of data, the original commons framework cannot be easily transposed from physical to data commons.
In her article, Tamar Sharon calls for a closer examination of the different conceptualizations of the common good that are at work in one specific area of the expanding health data ecosystem, what she calls the “Googlization of health research”, or the recent entrance of large consumer tech corporations into the medical domain. Using the framework of justification analysis (Boltanski and Thévenot, 2006), she identifies a plurality of conceptualizations of the common good that different actors mobilize to justify collaborating within these new multi-stakeholder research projects.
We hope that this special theme offers a productive – albeit far from comprehensive – overview of arguments for and examples of infrastructure, governance and ethics that are collective-centric in addressing the challenges posed by the datafication and expansion of the health ecosystem.
Andrejevic M (2014) The Big Data Divide. International Journal of Communication 8: 1673–89.
Boltanski L, and Thévenot, L (2006) On Justification: Economies of Worth. Princeton: Princeton University Press.
boyd d and Crawford K (2012) Critical Questions for Big Data. Information Communication & Society 15(5): 662–79.
Casilli A (2019) En Attendant les Robots: Enquête sur le Travail du Clic. Paris: Seuil.
Evans B (2016) Barbarians at the Gate: Consumer-Driven Health Data Commons and the Transformation of Citizen Science. American Journal of Law & Medicine (4): 1–34.
Health Data Exploration Project (HDE) (2014) Personal Data for the Public Good: New Opportunities to Enrich Understanding of Individual and Population Health. Calit2, UC Irvine and UC San Diego. Available at: at http://hdexplore.calit2.net/wp-content/uploads/2015/08/hdx_final_report_small.pdf
Kaye J, Whitley E, Lund D, Morrison M, Teare H and Melham K (2015) Dynamic consent: a patient interface for twenty-first century research networks. European Journal of Human Genetics 23(2): 141-146.
Kish L and Topol E (2015) Unpatients – why patients should own their medical data. Nature Biotechnology 33(9): 921-924.
Lanier J (2013) Who Owns the Future? London: Penguin Books.
Lucivero F and Prainsack B (2015) The lifestylisation of healthcare? “Consumer genomics” and mobile health as technologies for healthy lifestyle’. Applied and Translational Genomics, 4. http://dx.doi.org/10.1016/j.atg.2015.02.001.
Sharon T (2016) The Googlization of health research: from disruptive innovation to disruptive ethics. Personalized Medicine. DOI: 10.2217/pme-2016-0057.
Shen H (2015) Smartphones set to boost large-scale health studies. Nature. DOI:10.1038/nature.2015.17083.
Taylor L (2017) What is data justice? Big Data & Society 4(2): 1-14.
Taylor M (2012) Genetic Data and the Law: A Critical Perspective on Privacy Protection. Cambridge: Cambridge University Press.
Topol E (2015) The Patient Will See You Now: The Future of Medicine Is in Your Hands. New York: Basic Books.
van Dijck J, Poell T and de Waal M (2018) The Platform Society: Public Values in a Connective World. Oxford: Oxford University Press.
Weber GM, Mandl KD and Kohane IS (2014) Finding the missing link for big biomedical data. JAMA 311(24):2479–2480. doi:10.1001/jama.2014.4228
Zuboff S (2015) Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology 30(1): 75–89.