Paper by Fabrizio Dell’Acqua et al: “The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine the performance implications of AI on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company. After establishing a performance baseline on a similar task, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access,... (More >)
Citizens call for sufficiency and regulation — A comparison of European citizen assemblies and National Energy and Climate Plans
Paper by Jonas Lage et al: “There is a growing body of scientific evidence supporting sufficiency as an inevitable strategy for mitigating climate change. Despite this, sufficiency plays a minor role in existing climate and energy policies. Following previous work on the National Energy and Climate Plans of EU countries, we conduct a similar content analysis of the recommendations made by citizen assemblies on climate change mitigation in ten European countries and the EU, and compare the results of these studies. Citizen assemblies are representative mini-publics and enjoy a high level of legitimacy. We identify a total of 860... (More >)
Artificial intelligence in local governments: perceptions of city managers on prospects, constraints and choices
Paper by Tan Yigitcanlar, Duzgun Agdas & Kenan Degirmenci: “Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of AI is on the rise in urban policy circles, there is... (More >)
Data Commons
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... (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... (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... (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... (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... (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.... (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... (More >)
