Emerging models of data governance in the age of datafication

Abstract

The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe.

Knowledge co-creation in participatory policy and practice: Building community through data-driven direct democracy

“…For example, the United Kingdom’s ‘care.data’ project, which involved extracting data from primary care medical records, failed to gain social license despite following formal regulations, as it failed to meet societal expectations of privacy (Carter et al., 2015). Through the implementation of participatory data governance frameworks, institutions can involve citizens in establishing the principles by which data is used (Micheli et al., 2020). Models such as civic data trusts, where data are held by an independent party and who facilitate participation by stakeholders in decisions regarding data access, sharing and use, could reduce the barriers to data governance (Kariotis et al., 2020).…”

Inclusive Smart Cities: Beyond Voluntary Corporate Data Sharing

“…However, the paper is situated among, and related to, a number of related frameworks addressing the multiple types of partnerships between public, private and nongovernmental actors, as well as among citizens and data users themselves, to leverage and share data for positive societal impact. Those relevant literatures have produced a number of data governance models [35][36][37] and labels to refer to data sharing and usage arrangements, including 'data driven social partnerships' [38], 'data collaboratives' [39][40][41][42], 'platform cooperatives' [43][44][45][46][47][48][49], 'data trusts' [50][51][52] and 'data philanthropy' [53][54][55]. The phrase 'data for good' [56] is also used to underline the positive potential of data sharing.…”

“…Such initiatives build on arguments to empower citizens and reach 'technological sovereignty' in a datafied world that is political [27,35,[64][65][66][67] by presenting the possibilities of agency from the 'bottom up' [35,36,50,59,62,63,[68][69][70][71][72][73][74], 'good data practices' [75] and 'alternative data governance models' [34,61]. As such, a number of typologies and classifications have been presented in the scholarly and grey literatures to conceptualise the diversity of private data sharing practices [34,37,[76][77][78][79][80][81].…”

Artificial intelligence in business models as a tool for managing digital risks in international markets

“…AI has radical, massive and widespread impact on the society [13]. Therefore, companies and governments often invest heavily in automated systems in order to achieve benefits for society [14] and the best examples are China and the USA [15]. Many countries have started to invest in AI to gain competitive advantage and to help domestic companies face the global competition.…”