Paper by R. V. Guha et al: “Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be ‘joined’ easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the architecture of Data Commons, some of the major deployments and highlights directions for future work…(More)”.
Evidence-based policymaking in the legislatures
Blog by Ville Aula: “Evidence-based policymaking is a popular approach to policy that has received widespread public attention during the COVID-19 pandemic, as well as in the fight against climate change. It argues that policy choices based on rigorous, preferably scientific evidence should be given priority over choices based on other types of justification. However, delegating policymaking solely to researchers goes against the idea that policies are determined democratically.
In my recent article published in Policy & Politics: Evidence-based policymaking in the legislatures we explored the tension between politics and evidence in the national legislatures. While evidence-based policymaking has been extensively studied within governments, the legislative arena has received much less attention. The focus of the study was on understanding how legislators, legislative committees, and political parties together shape the use of evidence. We also wanted to explore how the interviewees understand timeliness and relevance of evidence, because lack of time is a key challenge within legislatures. The study is based on 39 interviews with legislators, party employees, and civil servants in Eduskunta, the national Parliament of Finland.
Our findings show that, in Finland, political parties play a key role in collecting, processing, and brokering evidence within legislatures. Finnish political parties maintain detailed policy programmes that guide their work in the legislature. The programmes are often based on extensive consultations with expert networks of the party and evidence collection from key stakeholders. Political parties are not ready to review these programmes every time new evidence is offered to them. This reluctance can give the appearance that parties do not want to follow evidence. Nevertheless, reluctance is oftens necessary for political parties to maintain stable policy platforms while navigating uncertainty amidst competing sources of evidence. Party positions can be based on extensive evidence and expertise even if some other sources of evidence contradict them.
Partisan expert networks and policy experts employed by political parties in particular appear to be crucial in formulating the evidence-base of policy programmes. The findings suggest that these groups should be a new target audience for evidence brokering. Yet political parties, their employees, and their networks have rarely been considered in research on evidence-based policymaking.
Turning to the question of timeliness we found, as expected, that use of evidence in the Parliament of Finland is driven by short-term reactiveness. However, in our study, we also found that short-term reactiveness and the notion of timeliness can refer to time windows ranging from months to weeks and, sometimes, merely days. The common recommendation by policy scholars to boost uptake of evidence by making it timely and relevant is therefore far from simple…(More)”.
The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from U.S. State Governments
Paper by Tzuhao Chen, Mila Gascó-Hernandez, and Marc Esteve: “Although the use of artificial intelligence (AI) chatbots in public organizations has increased in recent years, three crucial gaps remain unresolved. First, little empirical evidence has been produced to examine the deployment of chatbots in government contexts. Second, existing research does not distinguish clearly between the drivers of adoption and the determinants of success and, therefore, between the stages of adoption and implementation. Third, most current research does not use a multidimensional perspective to understand the adoption and implementation of AI in government organizations. Our study addresses these gaps by exploring the following question: what determinants facilitate or impede the adoption and implementation of chatbots in the public sector? We answer this question by analyzing 22 state agencies across the U.S.A. that use chatbots. Our analysis identifies ease of use and relative advantage of chatbots, leadership and innovative culture, external shock, and individual past experiences as the main drivers of the decisions to adopt chatbots. Further, it shows that different types of determinants (such as knowledge-base creation and maintenance, technology skills and system crashes, human and financial resources, cross-agency interaction and communication, confidentiality and safety rules and regulations, and citizens’ expectations, and the COVID-19 crisis) impact differently the adoption and implementation processes and, therefore, determine the success of chatbots in a different manner. Future research could focus on the interaction among different types of determinants for both adoption and implementation, as well as on the role of specific stakeholders, such as IT vendors…(More)”.
Governing the informed city: examining local government strategies for information production, consumption and knowledge sharing across ten cities
Paper by Katrien Steenmans et al: “Cities are more and more embedded in information flows, and their policies are increasingly called assessment frameworks to understand the impact of the systems of knowledge underpinning local government. Encouraging a more systemic view on the data politics of the urban age, this paper investigates the information ecosystem in which local governments are embedded. Seeking to go beyond the ‘smart city’ paradigm into a more overt discussion of the structures of information-driven urban governance, it offers a preliminary assessment across ten case studies (Barcelona, Bogotá, Chicago, London, Medellín, Melbourne, Mexico City, Mumbai, Seoul and Warsaw). It illustrates how both internal and external actors to local government are deeply involved throughout information mobilization processes, though in different capacities and to different extents, and how the impact of many of these actors is still not commonly assessed and/or leveraged by cities. Seeking to encourage more systematic analysis the governance of knowledge collection, dissemination, analysis, and use in cities, the paper advocates for an ‘ecosystem’ view of the emerging ‘informed cities’ paradigm…(More)”.
Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models
Paper by Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren: “The growing carbon footprint of artificial intelligence (AI) models, especially large ones such as GPT-3 and GPT-4, has been undergoing public scrutiny. Unfortunately, however, the equally important and enormous water footprint of AI models has remained under the radar. For example, training GPT-3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater (enough for producing 370 BMW cars or 320 Tesla electric vehicles) and the water consumption would have been tripled if training were done in Microsoft’s Asian data centers, but such information has been kept as a secret. This is extremely concerning, as freshwater scarcity has become one of the most pressing challenges shared by all of us in the wake of the rapidly growing population, depleting water resources, and aging water infrastructures. To respond to the global water challenges, AI models can, and also should, take social responsibility and lead by example by addressing their own water footprint. In this paper, we provide a principled methodology to estimate fine-grained water footprint of AI models, and also discuss the unique spatial-temporal diversities of AI models’ runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI…(More)”.
Incentivising open ecological data using blockchain technology
Paper by Robert John Lewis, Kjell-Erik Marstein & John-Arvid Grytnes: “Mindsets concerning data as proprietary are common, especially where data production is resource intensive. Fears of competing research in concert with loss of exclusivity to hard earned data are pervasive. This is for good reason given that current reward structures in academia focus overwhelmingly on journal prestige and high publication counts, and not accredited publication of open datasets. And, then there exists reluctance of researchers to cede control to centralised repositories, citing concern over the lack of trust and transparency over the way complex data are used and interpreted.
To begin to resolve these cultural and sociological constraints to open data sharing, we as a community must recognise that top-down pressure from policy alone is unlikely to improve the state of ecological data availability and accessibility. Open data policy is almost ubiquitous (e.g. the Joint Data Archiving Policy, (JDAP) http://datadryad.org/pages/jdap) and while cyber-infrastructures are becoming increasingly extensive, most have coevolved with sub-disciplines utilising high velocity, born digital data (e.g. remote sensing, automated sensor networks and citizen science). Consequently, they do not always offer technological solutions that ease data collation, standardisation, management and analytics, nor provide a good fit culturally to research communities working among the long-tail of ecological science, i.e. science conducted by many individual researchers/teams over limited spatial and temporal scales. Given the majority of scientific funding is spent on this type of dispersed research, there is a surprisingly large disconnect between the vast majority of ecological science and the cyber-infrastructures to support open data mandates, offering a possible explanation to why primary ecological data are reportedly difficult to find…(More)”.
Scaling deep through transformative learning in public sector innovation labs – experiences from Vancouver and Auckland
Article by Lindsay Cole & Penny Hagen: “…explores scaling deep through transformative learning in Public Sector Innovation Labs (PSI labs) as a pathway to increase the impacts of their work. Using literature review and participatory action research with two PSI labs in Vancouver and Auckland, we provide descriptions of how they enact transformative learning and scaling deep. A shared ambition for transformative innovation towards social and ecological wellbeing sparked independent moves towards scaling deep and transformative learning which, when compared, offer fruitful insights to researchers and practitioners. The article includes a PSI lab typology and six moves to practice transformative learning and scaling deep…(More)”.
Protests
Paper by Davide Cantoni, Andrew Kao, David Y. Yang & Noam Yuchtman: “Citizens have long taken to the streets to demand change, expressing political views that may otherwise be suppressed. Protests have produced change at local, national, and international scales, including spectacular moments of political and social transformation. We document five new empirical patterns describing 1.2 million protest events across 218 countries between 1980 and 2020. First, autocracies and weak democracies experienced a trend break in protests during the Arab Spring. Second, protest movements also rose in importance following the Arab Spring. Third, protest movements geographically diffuse over time, spiking to their peak, before falling off. Fourth, a country’s year-to-year economic performance is not strongly correlated with protests; individual values are predictive of protest participation. Fifth, the US, China, and Russia are the most over-represented countries by their share of academic studies. We discuss each pattern’s connections to the existing literature and anticipate paths for future work.Citizens have long taken to the streets to demand change, expressing political views that may otherwise be suppressed. Protests have produced change at local, national, and international scales, including spectacular moments of political and social transformation. We document five new empirical patterns describing 1.2 million protest events across 218 countries between 1980 and 2020. First, autocracies and weak democracies experienced a trend break in protests during the Arab Spring. Second, protest movements also rose in importance following the Arab Spring. Third, protest movements geographically diffuse over time, spiking to their peak, before falling off. Fourth, a country’s year-to-year economic performance is not strongly correlated with protests; individual values are predictive of protest participation. Fifth, the US, China, and Russia are the most over-represented countries by their share of academic studies. We discuss each pattern’s connections to the existing literature and anticipate paths for future work.Citizens have long taken to the streets to demand change, expressing political views that may otherwise be suppressed. Protests have produced change at local, national, and international scales, including spectacular moments of political and social transformation. We document five new empirical patterns describing 1.2 million protest events across 218 countries between 1980 and 2020. First, autocracies and weak democracies experienced a trend break in protests during the Arab Spring. Second, protest movements also rose in importance following the Arab Spring. Third, protest movements geographically diffuse over time, spiking to their peak, before falling off. Fourth, a country’s year-to-year economic performance is not strongly correlated with protests; individual values are predictive of protest participation. Fifth, the US, China, and Russia are the most over-represented countries by their share of academic studies. We discuss each pattern’s connections to the existing literature and anticipate paths for future work…(More)”.
City/Science Intersections: A Scoping Review of Science for Policy in Urban Contexts
Paper by Gabriela Manrique Rueda et al: “Science is essential for cities to understand and intervene on the increasing global risks. However, challenges in effectively utilizing scientific knowledge in decision-making processes limit cities’ abilities to address these risks. This scoping review examines the development of science for urban policy, exploring the contextual factors, organizational structures, and mechanisms that facilitate or hinder the integration of science and policy. It investigates the challenges faced and the outcomes achieved. The findings reveal that science has gained influence in United Nations (UN) policy discourses, leading to the expansion of international, regional, and national networks connecting science and policy. Boundary-spanning organizations and collaborative research initiatives with stakeholders have emerged, creating platforms for dialogue, knowledge sharing, and experimentation. However, cultural differences between the science and policy realms impede the effective utilization of scientific knowledge in decision-making. While efforts are being made to develop methods and tools for knowledge co-production, translation, and mobilization, more attention is needed to establish science-for-policy organizational structures and address power imbalances in research processes that give rise to ethical challenges…(More)”.
How Will the State Think With the Assistance of ChatGPT? The Case of Customs as an Example of Generative Artificial Intelligence in Public Administrations
Paper by Thomas Cantens: “…discusses the implications of Generative Artificial Intelligence (GAI) in public administrations and the specific questions it raises compared to specialized and « numerical » AI, based on the example of Customs and the experience of the World Customs Organization in the field of AI and data strategy implementation in Member countries.
At the organizational level, the advantages of GAI include cost reduction through internalization of tasks, uniformity and correctness of administrative language, access to broad knowledge, and potential paradigm shifts in fraud detection. At this level, the paper highlights three facts that distinguish GAI from specialized AI : i) GAI is less associated to decision-making process than specialized AI in public administrations so far, ii) the risks usually associated with GAI are often similar to those previously associated with specialized AI, but, while certain risks remain pertinent, others lose significance due to the constraints imposed by the inherent limitations of GAI technology itself when implemented in public administrations, iii) training data corpus for GAI becomes a strategic asset for public administrations, maybe more than the algorithms themselves, which was not the case for specialized AI.
At the individual level, the paper emphasizes the “language-centric” nature of GAI in contrast to “number-centric” AI systems implemented within public administrations up until now. It discusses the risks of replacement or enslavement of civil servants to the machines by exploring the transformative impact of GAI on the intellectual production of the State. The paper pleads for the development of critical vigilance and critical thinking as specific skills for civil servants who are highly specialized and will have to think with the assistance of a machine that is eclectic by nature…(More)”.