Conceptualizing the Data Republic as a Sustainable, Inclusive and Resilient Governance Model
By Stefano Calzati
Publisher’s Note: This blog introduces a related research article, which can be found in the Data & Policy journal here.
The European Union has advocated in various policy-orienting documents and pieces of legislation its willingness to pursue a citizen-centric approach to the governing of the digital transformation. Under such label falls a broad and somewhat fuzzy understanding on how data-driven technologies and services should be regulated, striking a balance between, on the one hand, the defense of individuals’ fundamental rights, and, on the other hand, the achievement of economic competitiveness, democratic participation, and environmental sustainability.
The (glamouring) concept of “citizen-centric” has also the function to position the EU on a different path from its main geopolitical competitors, notably the United States and China. The former adopts a corporate-driven approach based on market deregulation and favoring economic competitiveness among tech stakeholders and platforms; the latter favors a state-led approach which depends on authority-defined plans and strive to achieve global leadership in strategic technological sectors while maintaining state control over social and moral behaviors. On the one hand, while a corporate-driven approach has proven robust in fostering a diverse research and development scenario, it has also shown limitations concerning the strengthening (or also ex-novo creation) of socio-economical inequalities. On the other hand, a state-led approach is able to channel massive human, economic and technological resources in pre-identified targets; yet, this comes to the detriment of the diversification of investments and the lack of sufficient checks and balances as far as the action of state authorities is concerned.
On its part, beyond the mentioned discursive framing, the EU is de facto enabling the emergence of a Digital Single Market (DSM) as a federated data system that aims to establish a technically secure and legally compliant backbone for the economically profitable sharing of data in/across emerging data spaces. As such, the DSM tends to privilege:
- individuals (e.g. consumers and companies) over the (societal) collective > hence, the DSM is not socially sustainable
- private actors over the public sector and non-institutional actors > hence, the DSM is not inclusive
- technical over non-technical (e.g. literacies, trust, governance) aspects> hence, the DSM is not resilient
While a right-based standpoint might constitute the necessary baseline to individual autonomy, there is increasing evidence that this approach is insufficient to protect Europeans as a whole. For instance, Viljoen notes that the individualistic vision behind the current EU approach does not account for the relational nature of data and the consequent trade-off effects that data reuse involving two subjects might have on unaware third parties. Similarly, Smuha suggests taking inspiration from environmental law for tackling potential collective-level effects caused by digital transformation, such as the erosion of the functioning of the rule of law, which can be neither accounted for nor mitigated by current individualistic approaches to digital transformation.
On this wave, scholars have started to call for the design of a comprehensive approach to the digital transformation which moves away from either favoring certain actors over others — for example, citizens, public actors, and private actors — or prioritizing one value over others — oftentimes economic competitiveness over social inclusiveness or environmental sustainability. To do so, it has been suggested to first consider the digital transformation as an emerging ecosystem where actors and values are not only interdependent, but co-dependent, thus requiring a holistic approach; and second, it is necessary to devise strategies to keep the whole ecosystem in balance. At stake is the reconsideration of data governance from an actor-network approach to a systemic-procedural one.
From these premises, Calzati and van Loenen elaborate the idea of a fair data ecosystem in which the data interests of all actors are systemically taken into account and disentangled based on rules and mechanisms that adjudicate which values and actors are to be prioritized on a case-by-case basis — indeed, what they call a “Data Republic”. As Susskind notes, “to be a republican is to regard the central problem of politics as the concentration of unaccountable power.” Following up on this, a Data Republic is a governance model striving for:
- (socio-economic) Sustainability: balance between economic and social values, as well as individual and collective interests > hence, fair adjudication
- Inclusiveness: involvement of institutional (public and private) and non-institutional actors, especially citizens and local communities > hence, open representativeness
- Resilience: links between top-down & bottom-up stances; checks and balances among actors; organizational adaptation to sociotechnical changes > hence, accountable exercise of power
To operationalize the Data Republic, the authors propose to couple a Data Commons (DC) approach with Open Data (OD) frameworks and Spatial Data Infrastructures (SDIs). On the one hand, DC is regarded as a viable third path to market and/or state approaches to the managing of data, with the intent to reappropriate data by citizens and repurpose these data by keeping a societal outlook in sight. DC maintains a local and collective outlook by default; so far, however, these initiatives remain affected by limited replicability and scalability due to limited institutionalization.
On the other hand, OD and SDIs initiatives have consolidated over the last three decades backed up at both institutional and infrastructural levels. Data are considered open when they are not personal and they can be freely used, re-used, and re-distributed by anyone, at most restricted by the obligation to name sources and “share-alike”.
An SDI, instead, is a dynamic and multi-disciplinary architecture that allows for access, reuse, and sharing of spatial data. SDIs, then, tend to have a national dimension by design with public authorities responsible for coordinating ready access to and interoperable use of these data. Both OD and SDIs, while being institutionalized, miss the needed context-flexibility to respond to locals’ data needs and involve them in the provision of indigenous data. The coupling of OD and SDIs with DC, then, creates the enabling conditions for designing the main roles, rules, and mechanisms of the Data Republic as a fair data ecosystem.
To guarantee the consolidation of systemically fair data practices and the pooling of grassroot contributions, the Data Republic envisions a two-tier articulation, combining Public-led Data Trust (PDT), currently identified as a robust model for participatory data governance, with the design of (non-institutional) “data communes” (also called “mini-publics”)
On the one hand, a PDT favors institutionalization through “a public actor access[ing], aggregate[ing] and us[ing] data about its citizens, including data held by commercial entities, with which it establishes a relationship of trust.” Hence, a PDT is an organ that creates the conditions, under certain rules, for the commoning of data — including access, reuse, and managing — provided by a diverse array of actors: public, private, academics, citizens, and noninstitutional ones. In this respect, the PDT works as a catalyzer for actors who want to contribute to the data lake; as an enabler for funding and tech/legal capabilities; and a guarantor of the complying to the rules for data sharing.
On the other hand, to avoid forms of institutional lock-in, the data republic supports the formation of data communes. These are citizen-led groups that aggregate on a voluntary and/or temporary basis to have their voice heard on local matters often untapped by institutional actors. The data commune collects (quality) on a given matter data and — based on such provision — can become part of the PDT with one representative. Beyond that (and to avoid the possible lack of the needed skills to collect quality data), the Data Republic identifies the key figure of public data stewards.
De facto, data stewards mediate between the PDT and data communes. Notably, they: a) advise the PDT and data communes on data-related matters; b) support data capacity building within the public sector, as well as coordinate data literacy programs for local communities; and c) counsel public sector’s lawyers on tech-legal related matters.
Last, to remain faithful to the idea of the republic as a model for the accountable distribution of power, the Data Republic includes a board of arbitration, which is responsible for counselling and/or adjudicating contentious issues occurring at various scales, based on conflicting values, and/or across various actors. The board is formed by representatives of the data communes, the PDT, and data stewards. Figure 1 visualizes the four interdependent pillars of the Data Republic:
At stake is, above all, the ability of the model to foster systemic links between institutional and noninstitutional actors, as well as to negotiate between top-down and bottom-up processes, and disentangle both when needed. From a practical point of view, the city is a privileged locus for testing the Data Republic model not only because the city is a meso-dimension linking local and (supra)national levels, thus OD and SDIs frameworks with DC initiatives, but also because the city is at once a unique place of tech innovation and a major target of this same innovation.
At present, the model of the Data Republic is still at a high-level of abstraction and demands not only a more fine-grained operationalization at legal and technical levels, but also a cognizant design of long-term strategies for tackling organizational issues. At this stage, however, it is already possible to indicate some policy-oriented steps to favor the enactment of such model. Notably policy efforts are required to build long-term tech-legal capacity in the public sector; data literacy in citizenry; and trust across institutional and noninstitutional actors.
About the Author: Stefano Calzati is a researcher at the Delft University of Technology with an in interest in digital cultures, philosophy of technology and data governance.
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