Social data governance: Towards a definition and model

Abstract

With the surge in the number of data and datafied governance initiatives, arrangements, and practices across the globe, understanding various types of such initiatives, arrangements, and their structural causes has become a daunting task for scholars, policy makers, and the public. This complexity additionally generates substantial difficulties in considering different data(fied) governances commensurable with each other. To advance the discussion, this study argues that existing scholarship is inclined to embrace an organization-centric perspective that primarily concerns factors and dynamics regarding data and datafication at the organizational level at the expense of macro-level social, political, and cultural factors of both data and governance. To explicate the macro, societal dimension of data governance, this study then suggests the term “social data governance” to bring forth the consideration that data governance not only reflects the society from which it emerges but also (re)produces the policies and practices of the society in question. Drawing on theories of political science and public management, a model of social data governance is proposed to elucidate the ideological and conceptual groundings of various modes of governance from a comparative perspective. This preliminary model, consisting of a two-dimensional continuum, state intervention and societal autonomy for the one, and national cultures for the other, accounts for variations in social data governance across societies as a complementary way of conceptualizing and categorizing data governance beyond the European standpoint. Finally, we conduct an extreme case study of governing digital contact-tracing techniques during the pandemic to exemplify the explanatory power of the proposed model of social data governance.

Social data governance: From reflective practices to comparative synthesis

“…By addressing “social data governance” as the keyword of the special theme, we aim, more specifically, to advance the discussion on a contextual understanding of both the governing foundations and effects of data, dataism, and datafication (van Dijck, 2014) in different societies. While an explication of the keyword is detailed further in Liu's (2022) discussion of social data governance, here we suggest that this emphasis reminds us to pay particular attention to—and thus account for—the social dynamics that underpin and contextualize the design, operation, and promotion of quantified governing mechanisms in which information on social behaviors is collected, datafied, manipulated, and represented. To do so, we seek to avoid the pitfalls of the “asocial and ahistorical”—that is, unpacking data governance as “something operating outside of history and of specific sociopolitical, cultural, and economic contexts” (Milan and Treré, 2019: 325).…”

“…All four articles, in explicit and implicit ways, consider non-governmental actors (see the summary of "Governing actors" in Table 1), including citizens, students, administrative officers, programmers, and business executives, as relevant stakeholders in the governance of big data practices. While Liu (2022) and Tan and Lim (2022) remain largely focused on the governing agencies, what is at stake is the social dynamics, namely the political cultures and governing ideologies shaped by the long-term interactions between the government and the public. The four pieces also share an understanding of "the social" as an ensemble of actors and their interactions, app-mediated communications involved in the governing process, as opposed to the government, legislators, and regulators in sole charge of data governance work.…”

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

“…Although major challenges related to data governance/ management, 135,[175][176][177][178][179][180][181][182][183] ethical/legal, [184][185][186][187] and environmental 188,189 considerations would need to be addressed, these "dynamic multimodal ML models" may become cost-effective in different populations 169,[190][191][192][193] if conceived as the integrative tools needed to provide precision health 22,164,165,168,[194][195][196] and support some of the iterative cycles of knowledge generation and continuous improvement of "learning health systems". 11,[197][198][199][200]…”

Digital Innovation and Sustainable Development: Two Sides of the Same Coin

“…Digital innovation can, for example, support social action in favor of active aging: data from smart devices and home monitoring technologies can support individuals and families in tackling the challenges of aging at home [4,5]. Data have become the central resource for the creation of value and for decision-making processes at every level (company, family, institution), therefore also for policies: this brings out several new questions on how data should and can be governed to achieve social beneficial innovations by avoiding or mitigating unwanted outcomes [6][7][8].…”