Paper by Christopher Small et al: “Polis is a platform that leverages machine intelligence to scale up deliberative processes. In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements. In particular, we demonstrate with pilot experiments using Anthropic’s Claude that LLMs can indeed augment human intelligence to help more efficiently run Polis conversations. In particular, we find that summarization capabilities enable categorically new methods with immense promise to empower the public in collective meaning-making exercises. And notably, LLM context limitations have a significant impact on insight and quality of these results.
However, these opportunities come with risks. We discuss some of these risks, as well as principles and techniques for characterizing and mitigating them, and the implications for other deliberative or political systems that may employ LLMs. Finally, we conclude with several open future research directions for augmenting tools like Polis with LLMs….(More)”.
Artificial Intelligence for Emergency Response
Paper by Ayan Mukhopadhyay: “Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has been devoted to studying emergency incidents and response in the last several decades. In particular, data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch. We aim to present mathematical formulations for these problems and broad frameworks for each problem. We also share open-source (synthetic) data from a large metropolitan area in the USA for future work on data-driven emergency response…(More)”.
Open Data on GitHub: Unlocking the Potential of AI
Paper by Anthony Cintron Roman, Kevin Xu, Arfon Smith, Jehu Torres Vega, Caleb Robinson, Juan M Lavista Ferres: “GitHub is the world’s largest platform for collaborative software development, with over 100 million users. GitHub is also used extensively for open data collaboration, hosting more than 800 million open data files, totaling 142 terabytes of data. This study highlights the potential of open data on GitHub and demonstrates how it can accelerate AI research. We analyze the existing landscape of open data on GitHub and the patterns of how users share datasets. Our findings show that GitHub is one of the largest hosts of open data in the world and has experienced an accelerated growth of open data assets over the past four years. By examining the open data landscape on GitHub, we aim to empower users and organizations to leverage existing open datasets and improve their discoverability — ultimately contributing to the ongoing AI revolution to help address complex societal issues. We release the three datasets that we have collected to support this analysis as open datasets at this https URL…(More)”
Ethical Considerations Towards Protestware
Paper by Marc Cheong, Raula Gaikovina Kula, and Christoph Treude: “A key drawback to using a Open Source third-party library is the risk of introducing malicious attacks. In recently times, these threats have taken a new form, when maintainers turn their Open Source libraries into protestware. This is defined as software containing political messages delivered through these libraries, which can either be malicious or benign. Since developers are willing to freely open-up their software to these libraries, much trust and responsibility are placed on the maintainers to ensure that the library does what it promises to do. This paper takes a look into the possible scenarios where developers might consider turning their Open Source Software into protestware, using an ethico-philosophical lens. Using different frameworks commonly used in AI ethics, we explore the different dilemmas that may result in protestware. Additionally, we illustrate how an open-source maintainer’s decision to protest is influenced by different stakeholders (viz., their membership in the OSS community, their personal views, financial motivations, social status, and moral viewpoints), making protestware a multifaceted and intricate matter…(More)”
How Does Data Access Shape Science?
Paper by Abhishek Nagaraj & Matteo Tranchero: “This study examines the impact of access to confidential administrative data on the rate, direction, and policy relevance of economics research. To study this question, we exploit the progressive geographic expansion of the U.S. Census Bureau’s Federal Statistical Research Data Centers (FSRDCs). FSRDCs boost data diffusion, help empirical researchers publish more articles in top outlets, and increase citation-weighted publications. Besides direct data usage, spillovers to non-adopters also drive this effect. Further, citations to exposed researchers in policy documents increase significantly. Our findings underscore the importance of data access for scientific progress and evidence-based policy formulation…(More)”.
Collective Intelligence to Co-Create the Cities of the Future: Proposal of an Evaluation Tool for Citizen Initiatives
Paper by Fanny E. Berigüete, Inma Rodriguez Cantalapiedra, Mariana Palumbo and Torsten Masseck: “Citizen initiatives (CIs), through their activities, have become a mechanism to promote empowerment, social inclusion, change of habits, and the transformation of neighbourhoods, influencing their sustainability, but how can this impact be measured? Currently, there are no tools that directly assess this impact, so our research seeks to describe and evaluate the contributions of CIs in a holistic and comprehensive way, respecting the versatility of their activities. This research proposes an evaluation system of 33 indicators distributed in 3 blocks: social cohesion, urban metabolism, and transformation potential, which can be applied through a questionnaire. This research applied different methods such as desk study, literature review, and case study analysis. The evaluation of case studies showed that the developed evaluation system well reflects the individual contribution of CIs to sensitive and important aspects of neighbourhoods, with a lesser or greater impact according to the activities they carry out and the holistic conception they have of sustainability. Further implementation and validation of the system in different contexts is needed, but it is a novel and interesting proposal that will favour decision making for the promotion of one or another type of initiative according to its benefits and the reality and needs of the neighbourhood…(More)”.
Local Data Spaces: Leveraging trusted research environments for secure location-based policy research
Paper by Jacob L. Macdonald, Mark A. Green, Maurizio Gibin, Simon Leech, Alex Singleton and Paul Longely: “This work explores the use of Trusted Research Environments for the secure analysis of sensitive, record-level data on local coronavirus disease-2019 (COVID-19) inequalities and economic vulnerabilities. The Local Data Spaces (LDS) project was a targeted rapid response and cross-disciplinary collaborative initiative using the Office for National Statistics’ Secure Research Service for localized comparison and analysis of health and economic outcomes over the course of the COVID-19 pandemic. Embedded researchers worked on co-producing a range of locally focused insights and reports built on secure secondary data and made appropriately open and available to the public and all local stakeholders for wider use. With secure infrastructure and overall data governance practices in place, accredited researchers were able to access a wealth of detailed data and resources to facilitate more targeted local policy analysis. Working with data within such infrastructure as part of a larger research project involved advanced planning and coordination to be efficient. As new and novel granular data resources become securely available (e.g., record-level administrative digital health records or consumer data), a range of local policy insights can be gained across issues of public health or local economic vitality. Many of these new forms of data however often come with a large degree of sensitivity around issues of personal identifiability and how the data is used for public-facing research and require secure and responsible use. Learning to work appropriately with secure data and research environments can open up many avenues for collaboration and analysis…(More)”
TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI
Paper by Andrew Critch and Stuart Russell: “While several recent works have identified societal-scale and extinction-level risks to humanity arising from artificial intelligence, few have attempted an {\em exhaustive taxonomy} of such risks. Many exhaustive taxonomies are possible, and some are useful — particularly if they reveal new risks or practical approaches to safety. This paper explores a taxonomy based on accountability: whose actions lead to the risk, are the actors unified, and are they deliberate? We also provide stories to illustrate how the various risk types could each play out, including risks arising from unanticipated interactions of many AI systems, as well as risks from deliberate misuse, for which combined technical and policy solutions are indicated…(More)”.
Critical factors influencing information disclosure in public organisations
Paper by Francisca Tejedo-Romero & Joaquim Filipe Ferraz Esteves Araujo: “Open government initiatives around the world and the passage of freedom of information laws are opening public organisations through information disclosure to ensure transparency and encourage citizen participation and engagement. At the municipal level, social, economic, and political factors are found to account for this trend. However, the findings on this issue are inconclusive and may differ from country to country. This paper contributes to this discussion by analysing a unitary country where the same set of laws and rules governs the constituent municipalities. It seeks to identify critical factors that affect the disclosure of municipal information. For this purpose, a longitudinal study was carried out over a period of 4 years using panel data methodology. The main conclusions seem to point to municipalities’ intention to increase the dissemination of information to reduce low levels of voter turnout and increase civic involvement and political participation. Municipalities governed by leftist parties and those that have high indebtedness are most likely to disclose information. Additionally, internet access has created new opportunities for citizens to access information, which exerts pressure for greater dissemination of information by municipalities. These findings are important to practitioners because they indicate the need to improve citizens’ access to the Internet and maintain information disclosure strategies beyond election periods…(More)”.
The Generic Collective Intelligence Framework: A Qualitative View
Paper by Shweta Suray, Vishwajeet Pattanaik and Dirk Draheim: “Web-based crowd-oriented systems can be seen everywhere today. Some popular examples of such platforms include Reddit, MyGov, Wikipedia and GalaxyZoo. The main aim of such platforms is to harness individuals’ collective intelligence (CI) to offer several benefits to society — from solving complex problems to developing innovative solutions. Also, CI platforms are developing at an impressive pace, and each platform has unique features to gather, motivate and engage the crowd to achieve the platform’s goal. However, the design and development of CI systems is an expensive and time-consuming task. Thus, it is difficult for civic and government organizations to develop such platforms. Moreover, many CI platforms do not sustain for a longer period due to a lack of scientific knowledge about the several components that are required to build CI platforms. To fulfil this research gap, we developed a conceptual generic framework for CI systems that can be used to understand and examine CI systems regardless of their domains. Additionally, our CI generic framework allows stakeholders to combine several components in order to perform requirement elicitation for new platforms, thus enabling them to develop their own platforms in an efficient and effective manner. In order to evaluate the completeness and genericness and to find out the limitations of the CI generic model, we have conducted qualitative interviews with a series of CI experts. This article aims to present the findings of these expert interviews and suggest future directions for CI research to improve the generic CI model and to explore the CI from different perspectives…(More)”