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2019 ITU Kaleidoscope Academic Conference
Secondly, while deriving relevant insights from health justice. As noted by Taylor [27], the ends of various data
informatics primarily ensues through the individual’s justice formulations is to achieve both specific outcomes and
engagement with their data, research has found that also specific configurations of the associated data
individuals also engage in sharing of their data with others assemblages towards the achievement of those outcomes: in
for sense-making purposes [20]–[22]. Thus, the collection the case of Johnson’s [29] framework, the end goal is
and use of data by individuals also comprise the social embedding anti-discrimination principles and features in the
dimension. design of database systems; for Heeks and Renken [28], the
focus is on data distribution in a way that achieves fair access,
Thirdly, personal health data also gets shared to support participation and representation; and lastly Dencik et al. [30]
external pursuits such as biomedical research, where data on are interested in the means of limiting data collection and
health profiles, cohort data, as well as physical activity data distribution in contexts of surveillance capitalism.
can support projects such as the Global Alliance for
Genomics and Health [23]. The sharing of data in this In the work of Mortier et al. [31], in which they formalize
context can be motivated from the perspective of the the notion of human-data interaction (HDI), they explicate
Universal Declaration of Human Rights, which recognizes the interaction between humans and data systems in a way
the “right of everyone to share in scientific advancement and that places “the human at the center of the flows of data, and
its benefits” [23]. Thus sharing of data can be towards these providing mechanisms for citizens to interact with these
goals, which are associated with citizen science, as well as systems and data explicitly”. While the formulation of HDI
increased participation and engagement in advancing is not explicitly from a social justice nor ethics perspective,
scientific research [24]. it gives recognition to the fact that the underlying issues in
HDI sit at the intersection of “the various disciplines
In all these cases of external sharing of personal health data, including computer science, statistics, sociology,
there is, however, the persistent risk of “Googlization of psychology, and behavioral economics” [31]. Further, it
health research,” which is associated with the increasing gives recognition to the fact that human-data interaction
data-driven encroachment and involvement of the major happens in the context of complex data ecosystems, which
technology companies within the health and biomedical are constitutive of the global data-driven society. In this
sectors [17]. The potential benefits of the application of these complex interaction of different stakeholders with different
technological developments on issues of health and capabilities, interests and agendas, there is an ongoing
wellbeing are immense; they include major improvements in contestation for the voices of humans and human-centric
disease diagnosis, improving access to services through perspectives not to be marginalized and excluded. Some of
telehealth solutions and advancing the developmental the powerful and key actants within the health informatics
aspirations of achieving universal health coverage. The ecosystem include health-service providers, the health
challenges, however, are equally immense and are associated industry, as well as the non-human technology-related
not only with adverse health outcomes but also with negative actants, as has been highlighted by Sharon [17] regarding the
sociocultural and economic consequences. These challenges influence of the technology companies in the health data
are related to issues of bias, privacy [25], informed consent, research. Further highlighting the complexity, Morley and
context transgressions [26], health data commoditization, Floridi [16] offer a poignant critique of the techno-utopian
new power asymmetries and discriminations [27], data formulation of mHealth technologies as empowering devices
valorization and benefit-sharing, and the importation of and warn against the risk of medical paternalism. Privileging
digital capitalism practices into the health realm [17]. the position of the humans within the health informatics
ecosystem, as has been done in the HDI framework, allows
3. DATA JUSTICE IN HEALTH INFORMATICS for the critical investigation of issues towards an explicit goal
of enhancing the substantive freedoms of individuals to
Numerous definitions of “data justice” have been advanced achieve their desired health outcomes and enhancing their
in the literature, which fundamentally recognize the social health capabilities [32].
justice dynamics and impacts of the use of data in society.
Taylor [27] defines data justice as the “fairness in the way In this paper, the HDI framework has been adopted to frame
people are made visible, represented and treated as a result the discussion of the outworking of data justice in health
of the production of digital data.” In her formulation of data informatics systems. The paper expands on the imperatives
justice she decomposes the concept to three notions of of legibility, agency and negotiability to identify specific
(in)visibility – associated with access to representation, and considerations and non-functional requirements to inform
informational privacy; (dis)engagement with technology, the design of health informatics systems.
which is linked to sharing in data benefits as well as
autonomy in data choices; and to antidiscrimination, which 3.1 Legibility
is linked with the ability to challenge bias and preventing
discrimination. Heeks and Renken [28] define data justice Legibility is summarily defined as “being able to be
simply as “the primary ethical standard by which data- understood by people they concern, as a precursor to
related resources, processes and structures are evaluated.” exercising their agency” [31]. This is defined with regards to
They, however, expand this to formulate three notions of the data, as far as individuals understanding what data has
instrumental, procedural and distributive rights-based data been collected, how it is being used, by whom, and when it
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