Paper by Jenna Guffogg et al: “Plastic pollution on shorelines poses a significant threat to coastal ecosystems, underscoring the urgent need for scalable detection methods to facilitate debris removal. In this study, the Beached Plastic Debris Index (BPDI) was developed to detect plastic accumulation on beaches using shortwave infrared spectral features. To validate the BPDI, plastic targets with varying sub-pixel covers were placed on a sand spit and captured using WorldView-3 satellite imagery. The performance of the BPDI was analysed in comparison with the Normalized Difference Plastic Index (NDPI), the Plastic Index (PI), and two hydrocarbon indices (HI, HC). The BPDI successfully detected the plastic targets from sand, water, and vegetation, outperforming the other indices and identifying pixels with <30 % plastic cover. The robustness of the BPDI suggests its potential as an effective tool for mapping plastic debris accumulations along coastlines…(More)”.
Addressing Data Challenges to Drive the Transformation of Smart Cities
Paper by Ekaterina Gilman et al: “Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest…(More)”.
Artificial Intelligence, Scientific Discovery, and Product Innovation
Paper by Aidan Toner-Rodgers: “… studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of “idea-generation” tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization…(More)”.
Privacy during pandemics: Attitudes to public use of personal data
Paper by Eleonora Freddi and Ole Christian Wasenden: “In this paper we investigate people’s attitudes to privacy and sharing of personal data when used to help society combat a contagious disease, such as COVID-19. Through a two-wave survey, we investigate the role of personal characteristics, and the effect of information, in shaping privacy attitudes. By conducting the survey in Norway and Sweden, which adopted very different strategies to handle the COVID-19 pandemic, we analyze potential differences in privacy attitudes due to policy changes. We find that privacy concern is negatively correlated with allowing public use of personal data. Trust in the entity collecting data and collectivist preferences are positively correlated with this type of data usage. Providing more information about the public benefit of sharing personal data makes respondents more positive to the use of their data, while providing additional information about the costs associated with data sharing does not change attitudes. The analysis suggests that stating a clear purpose and benefit for the data collection makes respondents more positive about sharing. Despite very different policy approaches, we do not find any major differences in privacy attitudes between Norway and Sweden. Findings are also similar between the two survey waves, suggesting a minor role for contextual changes…(More)”
The need for climate data stewardship: 10 tensions and reflections regarding climate data governance
Paper by Stefaan Verhulst: “Datafication—the increase in data generation and advancements in data analysis—offers new possibilities for governing and tackling worldwide challenges such as climate change. However, employing data in policymaking carries various risks, such as exacerbating inequalities, introducing biases, and creating gaps in access. This paper articulates 10 core tensions related to climate data and its implications for climate data governance, ranging from the diversity of data sources and stakeholders to issues of quality, access, and the balancing act between local needs and global imperatives. Through examining these tensions, the article advocates for a paradigm shift towards multi-stakeholder governance, data stewardship, and equitable data practices to harness the potential of climate data for the public good. It underscores the critical role of data stewards in navigating these challenges, fostering a responsible data ecology, and ultimately contributing to a more sustainable and just approach to climate action and broader social issues…(More)”.
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
Paper by Andrey Bogomolov, Bruno Lepri, Jacopo Staiano, Nuria Oliver, Fabio Pianesi, and Alex Pentland: “In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders’ profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis…(More)”.
Determinants of behaviour and their efficacy as targets of behavioural change interventions
Paper by Dolores Albarracín, Bita Fayaz-Farkhad & Javier A. Granados Samayoa: “Unprecedented social, environmental, political and economic challenges — such as pandemics and epidemics, environmental degradation and community violence — require taking stock of how to promote behaviours that benefit individuals and society at large. In this Review, we synthesize multidisciplinary meta-analyses of the individual and social-structural determinants of behaviour (for example, beliefs and norms, respectively) and the efficacy of behavioural change interventions that target them. We find that, across domains, interventions designed to change individual determinants can be ordered by increasing impact as those targeting knowledge, general skills, general attitudes, beliefs, emotions, behavioural skills, behavioural attitudes and habits. Interventions designed to change social-structural determinants can be ordered by increasing impact as legal and administrative sanctions; programmes that increase institutional trustworthiness; interventions to change injunctive norms; monitors and reminders; descriptive norm interventions; material incentives; social support provision; and policies that increase access to a particular behaviour. We find similar patterns for health and environmental behavioural change specifically. Thus, policymakers should focus on interventions that enable individuals to circumvent obstacles to enacting desirable behaviours rather than targeting salient but ineffective determinants of behaviour such as knowledge and beliefs…(More)”
Innovation amnesia: Technology as a substitute for politics
Paper by Nathan Schneider: “…outlines a theory of amnesia in the face of innovation: when apparent technological innovations occasion the disregard of preexisting cultural, legal, and infrastructural norms. Innovation amnesia depends on cultural patterns that appear to be increasingly widespread: the valorization of technological innovation and the sensation of limited political space for reforming social arrangements. The resulting amnesia is by default an extension of existing structural inequalities. If innovations arise through deploying concentrated private wealth, the amnesia will likely target institutions that facilitate collective power among less powerful people. Up and down social hierarchies, however, achieving amnesia through innovation can bear irresistible allure. When other paths for structural change become mired in inertia or gridlock, amnesia may appear to be the only available pathway to reform. The purpose of a theory of amnesia is to assist affected communities in noticing it when it occurs and wielding it to their advantage, particularly through mobilizing self-governance around moments of innovation…(More)”.
The Rise of AI-Generated Content in Wikipedia
Paper by Creston Brooks, Samuel Eggert, and Denis Peskoff: “The rise of AI-generated content in popular information sources raises significant concerns about accountability, accuracy, and bias amplification. Beyond directly impacting consumers, the widespread presence of this content poses questions for the long-term viability of training language models on vast internet sweeps. We use GPTZero, a proprietary AI detector, and Binoculars, an open-source alternative, to establish lower bounds on the presence of AI-generated content in recently created Wikipedia pages. Both detectors reveal a marked increase in AI-generated content in recent pages compared to those from before the release of GPT-3.5. With thresholds calibrated to achieve a 1% false positive rate on pre-GPT-3.5 articles, detectors flag over 5% of newly created English Wikipedia articles as AI-generated, with lower percentages for German, French, and Italian articles. Flagged Wikipedia articles are typically of lower quality and are often self-promotional or partial towards a specific viewpoint on controversial topics…(More)”
AI and Data Science for Public Policy
Introduction to Special Issue by Kenneth Benoit: “Artificial intelligence (AI) and data science are reshaping public policy by enabling more data-driven, predictive, and responsive governance, while at the same time producing profound changes in knowledge production and education in the social and policy sciences. These advancements come with ethical and epistemological challenges surrounding issues of bias, transparency, privacy, and accountability. This special issue explores the opportunities and risks of integrating AI into public policy, offering theoretical frameworks and empirical analyses to help policymakers navigate these complexities. The contributions explore how AI can enhance decision-making in areas such as healthcare, justice, and public services, while emphasising the need for fairness, human judgment, and democratic accountability. The issue provides a roadmap for harnessing AI’s potential responsibly, ensuring it serves the public good and upholds democratic values…(More)”.