Anu Masso

This article explores the growing need to understand what skills are required to navigate rapid technological change driven by digitalisation, datafication, and AI – both within organisations and education, and in citizens’ everyday lives. Through a narrative literature review of digital, data, and AI literacy, we analyse their defining components, synthesising these approaches into an integrated framework. Our findings position digital literacy as a foundational concept essential for understanding and engaging with both data and AI literacy. While digital literacy equips individuals with basic technological skills, data literacy and AI literacy require more specialised knowledge. Data literacy involves using data for decision-making and problem-solving, whereas AI literacy extends to understanding AI systems, their functions, and their social, ethical, and legal implications. The article identifies three interrelated dimensions across all literacies: technical, critical, and communicative-cognitive. While technical and critical dimensions are well-documented, the communicative-cognitive dimension, essential for interacting with and cognitively relating to technological resources, remains less explored. We argue that educational programs must prioritise technical, critical thinking, and communicative-cognitive skills to cultivate comprehensive digital, data, and AI literacy. Finally, we raise critical questions about how public sector officials practise these literacies and how they can be integrated into education and training across diverse organisations.

Transborder data flows offer opportunities, such as health data sharing, but they also bring risk. Research has explored the tensions between transnational and regional linkages, striving to understand when transborder flows of data bring benefits or drawbacks. By viewing global data flows as a social change process, this commentary strives to complement existing perspectives. It advocates embedding data studies within the framework of social transformation theory to transcend the distinction between theory and its empirical application across diverse social and cultural contexts. Inspired by Stephen Castles’ approach to human migration, it introduces the concept of “social data migration” as a dynamic social transformation. This approach enhances our understanding of the complex, interconnected, and context-dependent nature of transnational flows of data across platforms amid rapid global changes.

Transborder data flows offer opportunities, such as health data sharing, but they also bring risk. Research has explored the tensions between transnational and regional linkages, striving to understand when transborder flows of data bring benefits or drawbacks. By viewing global data flows as a social change process, this commentary strives to complement existing perspectives. It advocates embedding data studies within the framework of social transformation theory to transcend the distinction between theory and its empirical application across diverse social and cultural contexts. Inspired by Stephen Castles’ approach to human migration, it introduces the concept of ‘social data migration’ as a dynamic social transformation. This approach enhances our understanding of the complex, interconnected, and context-dependent nature of transnational flows of data across platforms amid rapid global changes. data flows, data movements, data migration, digital economy, social transformation

Increasing attention is paid to ethical issues and values when designing and deploying artificial intelligence (AI). However, we do not know how those values are embedded in artificial artefacts or how relevant they are to the population exposed to and interacting with AI applications. Based on literature engaging with ethical principles and moral values in AI, we designed an original survey instrument, including 15 value components, to estimate the importance of these values to people in the general population. The article is based on representative surveys conducted in Estonia, Germany, and Sweden (n = 4501), which have varying experiences with implementing AI. The factor analysis showed four underlying dimensions of values embedded in the design and use of AI: (1) protection of personal interests to ensure social benefit, (2) general monitoring to ensure universal solidarity, (3) ensuring social diversity and social sustainability, and (4) efficiency. We found that value types can be ordered along the two dimensions of resources and change. The comparison between countries revealed that some dimensions, like social diversity and sustainability evaluations, are more universally valued among individuals, countries, and domains. Based on our analysis, we suggest a need and a framework for developing basic values in AI.