Social datafication, social transformations, research methods
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Scopus Publications
Scopus Publications
Comparing Digital, Data, and AI Literacy: A Narrative Review Jevgenia Polomoshnov, Anu Masso, Kateryna Lobanova Information Polity, 2026 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.
In the blind spot of AI-based mobility: eye-tracking visual attention to people with reduced mobility in autonomous vehicles and the ethics of inclusive design Mergime Ibrahimi, Anu Masso, Sven Kesselring, Kateryna Lobanova, Denis Newman-Griffis AI and Society, 2026 As AI systems increasingly shape urban mobility futures, the principles of equity, diversity, and inclusion have become central to responsible design. While race, gender, and age have been extensively studied, people with reduced mobility (PRM) remain underrepresented in empirical research on AI and autonomous vehicles (AVs). This article addresses this critical gap by investigating how disability and intersecting social categories are perceived and attended to in the visual framing of AV from an ethical standpoint. Our aim is to understand how people cognitively perceive PRM in automated decision-making contexts, and what this reveals about inclusive AI-based mobility design. We conducted a large-scale online eye-tracking experiment (N = 1272) combined with a survey to assess visual attention toward PRM in AV. Results reveal that PRM are consistently overlooked in visual attention compared to other demographic categories. Cluster analysis identified three distinct profiles of perception: High Disability Awareness, Adaptive Disability Evaluators, and Low Disability and Diversity Awareness, the latter encompassing most respondents. These insights were validated in an in-lab experiment with data science experts. Despite broad endorsement of fairness, justice, and diversity as guiding values in AV development, actual gaze behavior suggests a disconnect between normative commitments and perceptual engagement. We argue that this “blind spot” in AI-based mobility reflects deeper ableist assumptions embedded in algorithmic perceptions and raises ethical concerns. To counter this, we call for inclusive, cross-sectoral advisory boards that include PRM communities, ensuring that algorithmic designs not only reflect diversity but ethically see it.
Sociotechnical imaginaries of autonomous vehicles: Comparing laboratory and online eye-tracking methods Mergime Ibrahimi, Anu Masso, Mauro Bellone Plos One, 2025 This study investigates sociotechnical imaginaries of autonomous vehicles (AVs) using a dual approach: in-lab and online eye-tracking experiments. We examine how cognitive engagement varies across hypothetical decision-making scenarios involving algorithmic failure of AVs. In comparison with non-AV scenarios. This article highlights the characteristics, advantages, and limitations of methods, emphasizing their complementary contributions to understanding how individuals perceive and engage with emerging technologies. The in-lab experiment revealed high-quality and precise data from a homogeneous sample, while the online experiment enabled us to scale the research and explore diverse sociotechnical imaginaries from a global sample through crowd-sourced platforms. Key findings show that both in-lab and online participants exhibited longer gaze durations at one point, predominantly longer in AV scenarios. However, a deeper analysis of overall cognitive engagement revealed that in-lab participants, with more concentrated sociotechnical imaginaries, were more focused on non-AV scenarios, indicating a stronger emphasis on human decision-making. In contrast, online participants, whose imaginaries may be shaped by global perspectives and diverse experiences with data and algorithms, displayed increased attention toward AV scenarios, with significant visual variations among participants, reflecting global interest or concern over high-stakes algorithmic decisions. These findings contribute to our understanding of how perception of AVs differs globally and offer insights into emerging concerns around algorithmic decision-making in everyday life.
Using gamification to engage citizens in micro-mobility data sharing Anu Masso, Anniki Puura, Jevgenia Gerassimenko, Olle Järv Data in Brief, 2025 The European Strategy for Data aims to create a unified environment for accessing, sharing, and reusing data across sectors, institutions, and individuals, with a focus on areas like mobility and smart cities. While significant progress has been made in the technical interoperability and legislative frameworks for data spaces, critical gaps persist in the bottom-up processes, particularly in fostering social collaboration and citizen-driven initiatives. What is often overlooked is the need for effective citizen engagement and collaborative governance models to ensure the long-term viability and inclusivity of these data spaces. In addition, although principles for successful data sharing are well-established in sectors like healthcare, they remain underdeveloped and more challenging to implement in areas such as mobility. This article addresses these gaps by exploring how gamification can drive bottom-up data space formation, engaging citizens in data-sharing and fostering collaboration among private companies, local governments, and academic institutions. Using bicycle usage as an example, it illustrates how gamification can incentivise citizens to share mobility data for social good, promoting more active and sustainable transportation in cities. Drawing on a case study from Tallinn (Estonia), the paper demonstrates how gamification can improve data collection, highlighting the vital role of citizen participation in urban planning. The article emphasises that while technological solutions for data spaces are advancing, understanding collaborative governance models for data sharing remains crucial for ensuring the success of the European Union's data space agenda and driving sustainable innovation in urban environments.
Research ethics committees as knowledge gatekeepers: The impact of emerging technologies on social science research Anu Masso, Jevgenia Gerassimenko, Tayfun Kasapoglu, Mai Beilmann Journal of Responsible Technology, 2025 • Social science research ethics shift to discipline-specific principles, beyond procedural norms. • Procedural ethics face criticism; methods-based ethics offer contextually suited principles. • Emerging data technologies push adaptation of methods, and ethics in social science research. • Inequalities in knowledge production prompt scrutiny of ethics committees’ gatekeeping roles. • Tailored ethical guidelines are crucial for diverse methodologies in social science research. This article investigates the evolution of research ethics within the social sciences, emphasising the shift from procedural norms borrowed from medical and natural sciences to social scientific discipline-specific and method-based principles. This transformation acknowledges the unique challenges and opportunities in social science research, particularly in the context of emerging data technologies such as digital data, algorithms, and artificial intelligence. Our empirical analysis, based on a survey conducted among international social scientists (N = 214), highlights the precariousness researchers face regarding these technological shifts. Traditional methods remain prevalent, despite the recognition of new digital methodologies that necessitate new ethical principles. We discuss the role of ethics committees as influential gatekeepers, examining power dynamics and access to knowledge within the research landscape. The findings underscore the need for tailored ethical guidelines that accommodate diverse methodological approaches, advocate for interdisciplinary dialogue, and address inequalities in knowledge production. This article contributes to the broader understanding of evolving research ethics in an increasingly data-driven world.
(Un)predictable futures of policing: a social transformation approach Anu Masso, Tayfun Kasapoglu, Andrea Maccarini Critical Perspectives on Predictive Policing Anticipating Proof, 2025 As the ubiquity of data collection, analysis, and predictive analytics grows, it becomes crucial to comprehend the evolving interplay of fundamental structural, social, and cultural transformations. This chapter centres on predictive policing as a key welfare institution, using it as a lens to analyse these social shifts. The chapter introduces a theoretical-methodological framework for social transformations, emphasising the significance of social change in understanding technologies that rely on past data to shape the future. Examples from recent empirical studies are used to exemplify the analysis of social transformations linked to predictive policing. The first study investigates how international students perceive data collection practices when crossing borders through a semi-experimental eye-tracking study, aiming to discern norms and concerns. The second study explores narratives crafted by students in critical data studies to understand their expectations regarding the state and law enforcement's use of predictive technologies. Lastly, the chapter delves into biotechnology and, by focussing on the literature on biohacking, illuminates the relationship between changing bodies and social morphogenesis. The presented framework is posited to enhance our understanding of the implementation of novel data technologies by effectively tracing temporal threads associated with the application of predictive policing, leveraging its diachronically orientated conceptual framework.
Social Data Migration Concept: Analyzing Transborder Data Flows in the Post-Industrial Economy Anu Masso, Andrew Grotto, Tracey P. Lauriault Social Media and Society, 2025 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.
Automation scenarios: citizen attitudes towards automated decision-making in the public sector Anne Kaun, Anders Olof Larsson, Anu Masso Information Communication and Society, 2025 This article explores citizen attitudes towards automated decision-making (ADM) in the public sector, addressing concerns related to social justice and transparency. ADM, used in diverse public services, such as benefit application processing, welfare fraud detection and tax calculation, has sparked public interest and scepticism. To shed light on this complex issue and make ADM more accessible for citizens, we presented three domain-specific scenarios in a population-representative survey in Estonia (n = 1,500), Germany (n = 2,001) and Sweden (n = 1,000). These scenarios involved job seeker categorisation, child welfare risk assessment and predictive policing through facial recognition. Drawing from this survey and adopting an exploratory approach, we analyse attitudes across responses to these scenarios and conduct a regression analysis, integrating individual variables such as age, gender, education, awareness, enthusiasm and trust in ADM systems. Our findings reveal differences in citizens’ attitudes based on welfare regimes and individual characteristics. This citizen-focused approach underscores the significance of involving citizens in the governance of ADM in the digital welfare state, transcending the traditional regulatory and stakeholder-centric perspectives.
Basic values in artificial intelligence: comparative factor analysis in Estonia, Germany, and Sweden Anu Masso, Anne Kaun, Colin van Noordt AI and Society, 2024 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.
Automating public administration: citizens’ attitudes towards automated decision-making across Estonia, Sweden, and Germany Anne Kaun, Anders Olof Larsson, Anu Masso Information Communication and Society, 2024 Although algorithms are increasingly used for enabling the automation of tasks in public administration of welfare states, the citizens' knowledge of, experiences with and attitudes towards automated decision-making (ADM) in public administration are still less known. This article strives to reveal the perspectives of citizens who are increasingly exposed to ADM systems, relying on a comparative analysis of a representative survey conducted in Estonia, Germany, and Sweden. The findings show that there are important differences between the three countries when it comes to awareness, trust, and perceived suitability of ADM in public administration, which map onto historical differences in welfare provisions or so-called welfare regimes.
