Policy and Literacy for Data as Important Drivers to Trustworthy Governance

By Anushri Gupta, Sherman Kong, Mihoko Sumida, Gaby Umbach, Johanna Walker and Laura Zoboli

The changing landscape of technology in relation to its application and impact to societies and public institutions — and underlying dynamics from national policies to international cooperation in attempt to converge on principle-based and interest-preserved governance on its ongoing development — has seen increased debates driven by various framings coming into focus, the latest of which concern the potential of large-scale transformations on societal and economic systems brought on by the narratives of Digital Public Infrastructure and next-generation connectivity, and the promising yet also possibly problematic realities presented by Artificial Intelligence (AI). Fine-tuning and enacting on the right policies concerning data, and enhancing institutional know-how and literacy on its treatment and use, in turn becomes all the more paramount as data is key to either unlocking potential of these technologies or enabling further harm.

It is therefore important to guide global and public governance discourses through comprehensive examination of present and anticipatory data policies with related complex need for institutional data literacy, so that they may stay effective as discourses progress and newer use cases come to light. With this motivation, the “Policy & Literacy for Data” Area 3 Committee of the “Data & Policy” journal and community shares an immense responsibility to strategically curate insightful research and expert contribution both for the journal and now for the upcoming Data for Policy Conference in 2024, as one of the six overarching Standard Tracks. In order to inform this call for abstracts, full papers and panels, members of the Committee expound on this motivation below with their observation on relevant issues and thoughts on areas needed for generating new knowledge over six subtopics for, but not limited to, which we invite submissions:

1. Data governance, law and management of data
2. Design principles and impact assessment
3. Literacy, translation, communication
4. Intermediaries, trusts, collaboratives
5. Regulation of data-based services and processes
6. Open Science, open research infrastructure and FAIR practice

While looking forward to conference submissions from the academic and policy fields, we are in conjunction developing a landmark report unpacking further these 6 key topics, by examining trends and probing further issues that are under-focused within this landscape of policy and literacy for data. This analysis will help the field frame key questions emerging from this thematic discourse to inspire future research, reflection and engagement. Stay tuned for the next communique where we will share further the context and way(s) to participate.

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1) Data governance, law and management of data

In the digital era, data is often likened to oil, underscoring its role as a vital resource for businesses, governments, and individuals. As data multiplies and its importance grows, the intersections of data governance, law, and data management have become focal points of interest and concern. More importantly, as the digital era continues to evolve, the significance of understanding, adapting, and championing robust data governance cannot be overstated.

Whereas in the past, data governance was mainly addressed within corporate boundaries, focusing on ensuring data clarity, today the label ‘data governance’ refers to the overall management of the availability, integrity, usability and security of data as a dynamic framework extending from general international agreements to regional policies to individual data rights (Zygmuntowski, Zoboli and Nemitz, 2021). Based on the findings of Micheli et. al., 2020, among the data governance approaches suggested, we can recall “personal data sovereignty” which can be seen as being addressed by emerging collaborative treatment approach and partnerships on data (see section 4). To that extent, the legal regime of data governance is a hot topic of debate and many proposals emerged and are emerging to address the inadequacy of the status quo (Viljoen, 2020).

In light of this context, we welcome submissions in the following areas, among others:

• Definition and conceptualization of the role of data governance.
• Evolution and future directions of data governance paradigms.
• The role of international agreements and EU measures and national policies in shaping data governance.
• Legal ramifications, debates, and emerging proposals in data governance.
• The impact of data governance on individual data rights and privacy concerns.

Back to subtopics.

2) Design principles and impact assessment

With the adoption of data- and technology-driven solutions in governance and public services now spanning decades of experimentation and deployment with increasing maturity, an observable vast body of work has been dedicated to design principles by and initiatives on standard references for public institutions and industries, from the perspectives of both guiding technical implementations and policy design. These frameworks and design guidance include treatment and use of data, and are becoming more important in the context of digital government transformation and in particular the emerging approach of achieving such transformation through the establishment of Digital Public Infrastructure (Meltzer and Warren, 2022). They also exist to guide diplomacy and international cooperation settings concerning data exchange between countries and cross-border.

Some notable examples concern a set of technical specifications to exemplify the “building block” approach as developed and advocated by the GovStack initiative (UNF-DIAL, ITU, Estonia, Germany 2023), that likens to the “Digital Commons” model notionally put forward by European Union member states convened French presidency of the Council of the EU (EU Digital Assembly, 2022). These international or intergovernmental efforts to guide increasing complex ideas around interoperability both necessitate and facilitate more efficient practices in data treatment and use. In same light, the “Once Only” Principle stipulated by EU under the Single Digital Gateway Regulation (SDGR) comes into effect by the end of 2023 that mandates EU member states to provide a more seamless data gathering experience to citizens and users during interaction with public agencies and to enhance cross-border data-sharing between member states to strengthen the Digital Single Market directive (EC, 2020). Locally and nationally, inspirations can also be drawn from the likes of the United Kingdom government’s design principles on public digital services and their associated user experience (UK GDS, 2019), Estonia’s X-road on interoperability services to facilitate exchange of data between different agencies and industry entities (e-Estonia), also detailed technological and functional approaches towards designing public services and their treatment with data. Similarly, within international cooperation or ICT for Development (or ICT4D) contexts, the Principles of Digital Development also provide guidance and recommendation on the management and design of services in relation to treatment of data (Digital Impact Alliance, 2023).

Against this backdrop, we wish to increase policymakers and researchers’ awareness and knowledge concerning the mode of adoption and impact outcomes by the application of said principles and frameworks to hopefully systematically increase understanding of the value, challenge, and effects both within-administration and exogenous in practice. We welcome submissions to help deepen such understanding on the following suggested topics while not limiting to other contributions related to:

• Studies on effect(s) towards extant design principles given evolving governance models and interoperability activities or compliance between institutions and between states
• Analysis of impact by extant or anticipatory data practice and data design approaches towards intended principles (e.g. people-centricity, agency, cost-efficiency), towards outcomes (e.g. quality on delivery of services or institutional function), or inherent effects (e.g. on data asset ownership, organization of capacities and expertise within-firm / institution, etc.)
• Assessment / comparison on types of approach towards data ownership, sharing, treatment e.g. centralised vs. decentralised vs federated, etc.

Back to subtopics.

3) Literacy, translation, communication

Data literacy is a multidimensional, interdisciplinary concept that is of key importance to a variety of communities of practice. In a narrow understanding, it describes the ability to read, analyse, process and argue with data. This definition intertwines statistical literacy (Schield, 2011; see Collesi, 2019); science data literacy (Qin and D’Ignazio, 2010); model literacy and computational literacy (Schüller, 2020). Although this narrow view captures its essence, its societal importance extends its substance beyond technical data-related skills. Under this widened lens, data literacy is especially important at a governmental level for data-driven policy-making, where transparency, objectivity, and contestation are among key challenges (Umbach, 2020). It also strengthens the ability of individuals to understand and develop their personal arguments in vital policy areas that are subject to mis- and disinformation, such as climate change (Yalcinkaya, 2023; Walker et al., 2023). Data literate citizens understand their rights, opportunities and channels for participation and inclusion in a datafied society and have agency in the context of datafication and algorithm-based decision-making (Carmi et al., 2020).

However, these and other areas such as education have different focuses and concerns (Yousef, Walker and Leon-Urrutia, 2021) and the data literacy requirements of a particular application, such as policy-making, can be specific (Umbach, 2022). How best to address this in skills development is therefore a contested space and a matter of contextualisation. Skills-building in data literacy is often technology centred but there are an increasing number of creative non-digital approaches (D’Ignazio, 2017). Participatory data literacy methods addressing data criticality, datafication and data understanding include data walks (D’Ignazio, 2017; Powell, 2017; Mullagh and Walker, 2017). Arts and interactivity inspired awareness building and critical understanding approaches, which Pangrazio and Sefton-Green (2020) term ‘folk pedagogies of data’, have been employed successfully in the Global South (Bargarva et al., 2017). More recently, artists have used innovative ways to develop public critical understanding of data, algorithms and artificial intelligence, such as Imagenet Roulette. Policy support for critical AI art approaches to data literacy in the EU can be seen in projects such as MediaFutures.

To deepen our understanding of the role of data literacy in datafied societies, we encourage submission on the following areas, but not limited to them:

• Data literacy for participation and societal (inter-)actions in the datafied society: enabling agency, free will and data justice; Issues and challenges for policy in specific and general data literacies;
• Data literacy as a progressive solution to challenges to democracy;
• Forms of data-related literacies (statistical literacy, science data literacy, model literacy, algorithmic literacy, computational literacy);
• Data literacy of public and private actors: impact on participatory approaches in a datafied society;
• Policy approaches to folk pedagogies/artistic data literacy approaches;
• Case studies of effective data literacy programmes with empirical evidence.

Back to subtopics.

4) Intermediaries, trusts, collaboratives

Data intermediaries is a relatively contemporary and fragmented field, “with models ranging from individualistic and business-oriented types to more collective and inclusive models that support greater engagement in data governance by communities and individual data subjects” (Micheli et al., 2023). Different form factors have begun to emerge and policy in governing such federated approach to data treatment and ownership, e.g. the EU Data Governance Act applicable since Sept. 2023 requiring such intermediaries to be registered.

Studies so far have discussed the various organisational forms a data intermediary can take and the challenges therein. Organisational forms like data sharing pools, data trusts, data marketplaces, data cooperatives, data unions — (see for instance Gomer and Simperl, 2020; Hartman et al., 2020; Micheli et al., 2020); enabling governance mechanisms — technological platforms, APIs and legal regulatory contracts — (see for instance Janssen and Singh, 2022; Raetzsch et al., 2019); data ecosystem actors and multi-level interactions shaping varied data intermediary operating models and new forms of data institutions — (see for instance (Gupta et al., 2023; Open Data Institute, 2018; Reggi and Dawes, 2022; Gupta and Luca, 2021).

To further our understanding of the above, this topic invites submissions from all epistemological, ideological, and methodological standpoints. Be it conceptual papers, literature reviews or empirical studies, we encourage submissions with the following potential topics of interest, but not limited to:

• Understanding of data ecosystems — boundaries, structures, emergence of ecosystems — underpinning new forms of data institutions and data intermediaries.
• Policy implications of the issue of power and tensions within the politics of data and how this varies across different contexts.
• Mechanisms enabling orchestration of such data ecosystems.
• Strategies that would make data intermediaries an economically viable entity.

Back to subtopics.

5) Regulation of data-based services and processes

In the swiftly advancing digital era, data-driven services and processes have catalysed transformations in sectors from finance to healthcare and the regulatory framework pertaining to data-driven services, products, and processes is gaining prominence. Both national and European initiatives are emphasising measures to enhance their functioning, transparency, integrity, consistency, alongside ensuring accountability of data handlers (Kempeneer, 2021) and facilitating data access and sharing (Zoboli, 2020).

As we progress towards a more and more data-centric world, the urgency for robust and adaptable regulation of data-based services and processes will only escalate. Only anticipatory, adaptable regulatory actions can align with a future where innovation flourishes within a dependable and accountable structure. In this context, it’s pivotal to assess existing, proposed, and aspirational regulatory strategies.

At the European level, for example (Zech, 2016), overarching regulations that focus on the ‘data’ element (e.g., the proposal for the Data Act) or on specific areas of protection (e.g., GDPR) coexist with sectoral rules that influence the operational dynamics of certain data-based services(e.g., MIFID II). One might note, for instance, that the Payment Service Directive II has enabled fintech startups to access account data managed by incumbents, contingent upon user consent (Borgogno and Colangelo, 2020). This coexistence makes it difficult for service providers, when they consider introducing AI into their business, because a ‘transparency’ requirement which can meet all the relevant regulations is still an open question (Ostmann and Dorobantu, 2021). Parallel to this, pioneering legal concepts are gaining ground, both within legislative realms and academic discourse. For example, regulatory sandboxes that allow innovators to test new data-centric models in controlled settings (Allen, 2019), ensuring adherence without curtailing innovation. Data spaces (Horgan et al., 2022), and frameworks of data trusts, Data commons are also on the rise (Wong, Henderson and Ball, 2022).

In light of this context, we welcome submissions in the following areas, among others:

• Evolution of EU the regulatory framework data-driven services, products and processes.
• Transparency and explainability of data-driven processes.
• Data-driven services and privacy concerns.
• Regulation, access to data and innovation.
• Sectoral regulation on data-driven services, products and processes.
• Pioneering regulatory models impacting data-driven services, products and processes (e.g. regulatory sandboxes, data spaces, data trusts, data commons).

Back to subtopics.

6) Open Science, open research infrastructure and FAIR practice

Open Science, open research infrastructure, and FAIR (Findable, Accessible, Interoperable and Reusable) practice aim at greater accessibility of scientific output, scholarly data to increase the quality and reproducibility of research results. They revolutionise how research is conceptualised, conducted, disseminated and published; and impact on the ways in which research data are created, annotated, curated, managed, shared, reproduced, (re-)used, and further developed (Stracke, 2020). In this way, they represent a shift in the way that research is conducted and communicated (da Luz Antunes et al., 2020) and create a more open and collaborative research environment that benefits both researchers and society (Margoni, Caso, Ducato, Guarda and Moscon, 2016).

Especially the FAIR (Findable, Accessible, Interoperable, and Reusable) publishing principles significantly impact academic writing, particularly with a view to data sharing and open access. They aim to promote greater transparency, accessibility, and interoperability of research data and outputs. FAIR publishing objectives triggered the development of new open research infrastructure, publishing models and platforms (Rentier 2016), such as open access journals and preprint servers (Chakravorty et al., 2022). FAIR practice also encourages greater attention to metadata and data management practices (ensuring data quality (Sadiq and Indulska, 2017), creating standardised data formats, and developing clear documentation and data sharing policies) (Pampel and Dallmeier-Tiessen, 2014). Researchers are expected to provide detailed and standardised metadata descriptions for their research and publications, in order to make them more easily accessible.

Data generated by citizen science groups have become an increasingly important source for scientists particularly with regard to the Sustainable Development Goals (de Sherbinin et al., 2021). While open science is based on guiding principles of knowledge sharing that have been central to academia since the 17th century, the FAIR principles and data management are new constructs, and therefore, not necessarily in common practice. This is particularly true for citizen science groups (Bowser et al., 2020).

To enhance insight into the impact of open science, open research infrastructure and FAIR, we invite submission on the following areas, but not limited to them:

• Data literacy requirements in science, quality of data science and data-driven research.
• Metadata and data management practice.
• Dissemination of research data and FAIR publishing principles as well as practice.
• Societal impact of Open Science, open research infrastructure, and FAIR practice.
• Approaches to open science and FAIR practice in participatory data and citizen science.

Back to subtopics.

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If you are working on one or several of the above topics, please submit an abstract or full paper proposal. As a dynamic developing community, we always look for thought-provoking and fresh perspectives that can inspire future research and solutions for data governance, regulation, design, literacy, and communication. Contribute to our framing of Area 3 and co-create knowledge on policy and literacy for data during the Data for Policy Conference 2024.

About the Authors / Committee Members

Anushri Gupta (London School of Economics) is a postdoctoral research fellow at the London School of Economics and serves as an area editor for the Data & Policy journal and its associated conference Data for Policy on a specialised track on “Focus on the Policy Frameworks, Governance and Management of Data-driven Innovations”. She also serves as a member of the Digital Twins for Sustainable Development Goals (DT4SDG) lab at QMUL. Her research interests lie at the intersection of management and emerging digital technologies. She holds a PhD from the School of Business and Management, Queen Mary University of London and a Masters in Electronic Engineering with Business Management from King’s College London.

Sherman Kong (formerly United Nations Foundation) is the current Lead Editor of the “Policy & Literary for Data” area at the “Data & Policy” Cambridge journal and community. He formerly served as Senior Advisor at the Digital Impact Alliance of UNF, and partnered with the UN system, and national and intergovernmental bodies of the EU and AU to found and co-lead the multilateral “GovStack” initiative. He had overseen convergence initiatives primarily in international digital cooperation and global health, and had worked with various UN agencies, INGOs and private sector on innovation, data, policy, and management issues.

Mihoko Sumida (Hitotsubashi University) is a Professor of Civil Law at Hitotsubashi Institute of Advanced Studies (HIAS). She researches private law theories that support the redress of consumers and citizens, whose disadvantages tend to be concentrated due to changes of economics and society. In recent years, she explores the legal challenges posed by innovative technologies such as AI and the potential of AI-based legal research.

Gaby Umbach (European University Institute) is Part-time Professor at the EUI’s Robert Schuman Centre for Advanced Studies where she leads the Global Governance Programme’s research area on ‘Knowledge, Governance, Transformations’. She is also Founding Director of GlobalStat, elected member of the International Statistical Institute and chairs Area 3 on ‘Policy & Literacy for Data’ of the Data for Policy Community. Her current research focuses on the interaction between knowledge, evidence, data, and governance.

Johanna Walker (King’s College London) researches socio-technical aspects of data and AI with a particular focus on data-driven innovation and creativity in business and the arts, and ensuring this benefits data subjects. She co-chaired the 1st, 2nd and 3rd Data Literacy Workshops and co-edited the Journal of Community Informatics Special Issue on Data Literacy.

Laura Zoboli (University of Brescia) is Assistant Professor of Commercial Law at the University of Brescia and serves as Scientific Coordinator of the Centre for Antitrust and Regulatory Studies at the University of Warsaw. She earned her PhD from Bocconi University, was a Marie Curie Fellow at the University of Turin and a visiting researcher at the Max Planck Institute for Innovation and Competition and the Berkman Klein Center at Harvard University. Her main areas of research encompass intellectual property and competition law, with a specific focus on identifying the correlation between these legal domains and the evolution of the data economy.

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This is the blog for Data & Policy (cambridge.org/dap), a peer-reviewed open access journal exploring the interface of data science and governance. Read on for five ways to contribute to Data & Policy.