Anne M. Dijkstra

Artificial intelligence operates in a social context and affects everyone. Organizing science communication and engagement activities on the topic for all groups in society is therefore recommendable. However, research shows that mainly higher educated, already engaged citizens participate in science communication activities. To address this gap, our study collected insights from underrepresented citizens in the Netherlands regarding their views on and participation in science communication activities related to artificial intelligence. Using semi-structured interviews (n = 19), we qualitatively explored reasons for (non)participation. Participants knew more about AI than they initially reported, and worldviews were evoked in expressing both concerns and curiosity about AI. AI was deemed relevant but news about the topic was avoided due to a lack of trust. Non participation in engagement or communication activity was driven by both practical and social barriers. Following participants’ advice, we suggest a personal approach could help to make these activities more inviting.

Machine learning models using Earth observation data are vital for mapping deprived urban areas, enabling scalable application and transfer across cities and time where ground data is scarce. Yet, their practical impact is often hindered by reduced user trust in the underlying data quality. A scoping review of deprived urban area mapping studies reveals systemic shortcomings in spatial data quality practices, even if studies report high performance accuracy. Shortcomings include a lack of operationalizing semantic definitions to reference labels, difficulty in reproducibility due to restricted data access, and sampling strategies that ignore spatial autocorrelation. Together, these issues can inflate reported performance estimates and overstate real-world generalization. To address these challenges, this study introduces a Spatial Data-Centric Quality Assessment framework. The framework extends established geographic information standards and existing practices in relevant domains to provide suggested reporting items for data provenance, primary use case of the dataset, semantic consistency, spatiotemporal consistency, representativeness, annotation quality, and critical ethical considerations to ensure the protection of vulnerable populations during mapping. The framework bridges the gap between technical research and practical application by mandating transparent documentation and rigorous evaluation of spatial data quality. It ensures that datasets used for machine learning are reliable, transparent, and trustworthy to inform urban policy and planning.

Machine learning models using Earth observation data are vital for mapping deprived urban areas, enabling scalable application and transfer across cities and time where ground data is scarce. Yet, their practical impact is often hindered by reduced user trust in the underlying data quality. A scoping review of deprived urban area mapping studies reveals systemic shortcomings in spatial data quality practices, even if studies report high performance accuracy. Shortcomings include a lack of operationalizing semantic definitions to reference labels, difficulty in reproducibility due to restricted data access, and sampling strategies that ignore spatial autocorrelation. Together, these issues can inflate reported performance estimates and overstate real-world generalization. To address these challenges, this study introduces a Spatial Data-Centric Quality Assessment framework. The framework extends established geographic information standards and existing practices in relevant domains to provide suggested reporting items for data provenance, primary use case of the dataset, semantic consistency, spatiotemporal consistency, representativeness, annotation quality, and critical ethical considerations to ensure the protection of vulnerable populations during mapping. The framework bridges the gap between technical research and practical application by mandating transparent documentation and rigorous evaluation of spatial data quality. It ensures that datasets used for machine learning are reliable, transparent, and trustworthy to inform urban policy and planning.

Events such as the climate crisis, developments in artificial intelligence (AI) and the COVID-19 pandemic, compel interaction between researchers and journalists to inform citizens and increase public understanding of science. This literature review provides an in-depth analysis of publications about the changing relationship between researchers and journalists. The analysis is based on two main trends which impact science-media interactions, changing media landscapes and medialisation of science. We find that the roles of both researchers and journalists have diversified in response to increased competition and increased time pressure for the science journalism profession. The combination of the two trends may cause journalists to increasingly depend on strategic communication from researchers and public relations departments of scientific organizations, which could reduce the quality of science journalism. Additional research is recommended, in particular regarding how journalists and researchers can ensure science journalism of good quality with an emphasis on analysing understudied countries and recent developments in generative AI.

The global climate crisis compels nations to pursue clean and sustainable energy sources to meet the demands of both economic and decarbonisation goals. Geothermal energy is a critical component of this transition, yet its utilisation is often hindered by varying public perceptions. News media content plays a pivotal role in shaping risk perceptions of deep geothermal energy exploration and production. Despite research into how text-based news media influences public perception, there is a notable gap in understanding the extent to which visual framing shapes public perceptions and attitudes towards geothermal energy. This research employs an image cluster approach to analyse how geothermal energy is visually framed in news media in the Philippines, Kenya, Germany, and Australia. We also examine whether visual representations include or marginalize local communities. By categorizing visual motifs ranging from industrial techno-optimism to localized environmental impacts and comparing across various contexts, we explore how visual narratives may shape perceived acceptability of deep geothermal projects. This work advocates the inclusion of social science within transition pathway design, ensuring that modelled scenarios of the energy transition are grounded on social reality, making them not only technically feasible but socially just and inclusive.

Environmental hazards are key determinants of urban liveability, shaping the safety, health, and resilience of residents. This study investigates the intersection of urban livability and flood exposure by integrating remote sensing, citizen science, and AI-driven analysis across three African countries: Ghana, Kenya, and Mozambique. Using Sentinel-1 satellite imagery, open geospatial datasets, and advanced deep learning techniques, a citizen-derived perceived livability index was created which was then combined with rapid flood exposure modelling through FastFlood. The results reveal that areas with the lowest livability scores -characterized by poor housing conditions, limited service access, and minimal green spaces- are also consistently the most exposed to frequent and severe flooding. In Nairobi, for instance, approximately 35% of built-up areas are flood-prone, with informal settlements like Kibera and Mathare facing disproportionate risks. Citizen science efforts validated the flood models, underscoring the critical role of local knowledge in capturing fine-scale flood dynamics invisible to remote sensing alone. The project demonstrates that liveability and environmental risk are deeply interrelated, and contribute to worsening urban vulnerability. By combining community mapping with scalable Earth Observation methods, this work delivers actionable methods for urban planners, humanitarian organizations, and local policymakers. Our results stress the importance of planning strategies that prioritize investments in flood mitigation, nature-based solutions, and resilient infrastructure for the most at-risk communities. Such communities are often omitted in official data. The needs and views of such vulnerable communities need to be included in supporting sustainable and inclusive urban development under increasing climate pressures.

Science journalists, traditionally, play a key role in delivering science information to a wider audience. However, changes in the media ecosystem and the science-media relationship are posing challenges to reliable news production. Additionally, recent developments such as ChatGPT and Artificial Intelligence (AI) more generally, may have further consequences for the work of (science) journalists. Through a mixed-methodology, the quality of news reporting was studied within the context of AI. A content analysis of media output about AI (news articles published within the time frame 1 September 2022–28 February 2023) explored the adherence to quality indicators, while interviews shed light on journalism practices regarding quality reporting on and with AI. Perspectives from understudied areas in four European countries (Belgium, Italy, Portugal, and Spain) were included and compared. The findings show that AI received continuous media attention in the four countries. Furthermore, despite four different media landscapes, the reporting in the news articles adhered to the same quality criteria such as applying rigour, including sources of information, accessibility, and relevance. Thematic analysis of the interview findings revealed that impact of AI and ChatGPT on the journalism profession is still in its infancy. Expected benefits of AI related to helping with repetitive tasks (e.g. translations), and positively influencing journalistic principles of accessibility, engagement, and impact, while concerns showed fear for lower adherence to principles of rigour, integrity and transparency of sources of information. More generally, the interviewees expressed concerns about the state of science journalism, including a lack of funding influencing the quality of reporting. Journalists who were employed as staff as well as those who worked as freelancers put efforts in ensuring quality output, for example, via editorial oversight, discussions, or memberships of associations. Further research into the science-media relationship is recommended.

Co-creation, a form of engagement in which stakeholders jointly generate value, has increasingly received attention as a way to enhance responsible research and innovation (RRI). To explore the extent to which co-creation might support RRI, this paper analyses three co-creation workshops with different types of stakeholders in the area of nanotechnology for health. The analysis considers the potential of co-creation to enhance the legitimacy of and add value to innovation based on the four dimensions of RRI: reflexivity, inclusion, anticipation, and adaptation. The results show that it is difficult to address all the dimensions at once. A trade-off between creating legitimacy and added value can be detected: a co-creation process focusing on added value requires deliberation early in the innovation process, a certain specificity and an action-oriented perspective. This comes at the cost of the inclusion and anticipation of societal values, which are important dimensions of a legitimate innovation process.