Paper by Yuan Luo et al: “We piloted using Large Language Models (LLMs) for organizing AMIA 2024 Informatics Summit. LLMs were prompt engineered to develop algorithms for reviewer assignments, group presentations into sessions, suggest session titles, and provide one-sentence summaries for presentations. These tools substantially reduced planning time while enhancing the coherence and efficiency of conference organization. Our experience shows the potential of generative AI and LLMs to complement human expertise in academic conference planning…(More)”.
Cloze Encounters: The Impact of Pirated Data Access on LLM Performance
Paper by Stella Jia & Abhishek Nagaraj: “Large Language Models (LLMs) have demonstrated remarkable capabilities in text generation, but their performance may be influenced by the datasets on which they are trained, including potentially unauthorized or pirated content. We investigate the extent to which data access through pirated books influences LLM responses. We test the performance of leading foundation models (GPT, Claude, Llama, and Gemini) on a set of books that were and were not included in the Books3 dataset, which contains full-text pirated books and could be used for LLM training. We assess book-level performance using the “name cloze” word-prediction task. To examine the causal effect of Books3 inclusion we employ an instrumental variables strategy that exploits the pattern of book publication years in the Books3 dataset. In our sample of 12,916 books, we find significant improvements in LLM name cloze accuracy on books available within the Books3 dataset compared to those not present in these data. These effects are more pronounced for less popular books as compared to more popular books and vary across leading models. These findings have crucial implications for the economics of digitization, copyright policy, and the design and training of AI systems…(More)”.
On Democratic Organizing and Organization Theory
Paper by Julie Battilana, Christine M. Beckman, and Julie Yen: “As threats to democracy endanger the rights and freedoms of people around the world, scholars are increasingly interrogating the role that organizations play in shaping democratic and authoritarian societies. Just as societies can be more or less democratic, so, too, can organizations. This essay, in honor of ASQ’s 70th volume, argues for a deeper focus in organizational research on the extent to which organizations themselves are democratic and the outcomes associated with these varied models of organizing. First, we provide a framework for considering the extent to which organizations are democratically organized, accounting for the varied ways in which workers can participate in their organizations. Second, we call for research on the outcomes associated with democratic organizing at both the organizational and societal levels. We build from research arguing that the extent to which workers participate in organizational decision making can spill over to impact their expectations of and participation in civic life. Moving forward, we argue it is critical to recognize that questions of democracy and authoritarianism concern not only the political contexts in which organizations are embedded but also how organizations themselves are structured and contribute to society…(More)”
Large AI models are cultural and social technologies
Essay by Henry Farrell, Alison Gopnik, Cosma Shalizi, and James Evans: “Debates about artificial intelligence (AI) tend to revolve around whether large models are intelligent, autonomous agents. Some AI researchers and commentators speculate that we are on the cusp of creating agents with artificial general intelligence (AGI), a prospect anticipated with both elation and anxiety. There have also been extensive conversations about cultural and social consequences of large models, orbiting around two foci: immediate effects of these systems as they are currently used, and hypothetical futures when these systems turn into AGI agents perhaps even superintelligent AGI agents.
But this discourse about large models as intelligent agents is fundamentally misconceived. Combining ideas from social and behavioral sciences with computer science can help us understand AI systems more accurately. Large Models should not be viewed primarily as intelligent agents, but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated.
The new technology of large models combines important features of earlier technologies. Like pictures, writing, print, video, Internet search, and other such technologies, large models allow people to access information that other people have created. Large Models – currently language, vision, and multi-modal depend on the fact that the Internet has made the products of these earlier technologies readily available in machine-readable form. But like economic markets, state bureaucracies, and other social technologies, these systems not only make information widely available, they allow it to be reorganized, transformed, and restructured in distinctive ways. Adopting Herbert Simon’s terminology, large models are a new variant of the “artificial systems of human society” that process information to enable large-scale coordination…(More)”
A Quest for AI Knowledge
Paper by Joshua S. Gans: “This paper examines how the introduction of artificial intelligence (AI), particularly generative and large language models capable of interpolating precisely between known data points, reshapes scientists’ incentives for pursuing novel versus incremental research. Extending the theoretical framework of Carnehl and Schneider (2025), we analyse how decision-makers leverage AI to improve precision within well-defined knowledge domains. We identify conditions under which the availability of AI tools encourages scientists to choose more socially valuable, highly novel research projects, contrasting sharply with traditional patterns of incremental knowledge growth. Our model demonstrates a critical complementarity: scientists strategically align their research novelty choices to maximise the domain where AI can reliably inform decision-making. This dynamic fundamentally transforms the evolution of scientific knowledge, leading either to systematic “stepping stone” expansions or endogenous research cycles of strategic knowledge deepening. We discuss the broader implications for science policy, highlighting how sufficiently capable AI tools could mitigate traditional inefficiencies in scientific innovation, aligning private research incentives closely with the social optimum…(More)”.
Climate Assemblies and the Law: A Research Roadmap
Article by Leslie Anne and Duvic Paoli: “The article is interested in the relationship between citizens’ assemblies on climate change (‘climate assemblies’) and the law. It offers a research roadmap on the legal dimensions of climate assemblies with the view to advancing our knowledge of deliberative climate governance. The article explores six fundamental areas of inquiry on which legal scholarship can offer relevant insights. They relate to: i) understanding the outcomes of climate assemblies; ii) clarifying their role in the public law relationship between individuals and government; iii) gaining insights into the making of climate legislation and other rules; iv) exploring the societal authority of norms; v) illustrating the transnational governance of climate change, including the diffusion of its norms and vi) offering a testing ground for the design of legal systems that are more ecologically and socially just. The aim is to nudge legal scholars into exploring the richness of the questions raised by the emergence of climate assemblies and, in turn, to encourage other social science scholars to reflect on how the legal perspective might contribute to better understanding their object of study…(More)”.
Generative AI in Transportation Planning: A Survey
Paper by Longchao Da: “The integration of generative artificial intelligence (GenAI) into transportation planning has the potential to revolutionize tasks such as demand forecasting, infrastructure design, policy evaluation, and traffic simulation. However, there is a critical need for a systematic framework to guide the adoption of GenAI in this interdisciplinary domain. In this survey, we, a multidisciplinary team of researchers spanning computer science and transportation engineering, present the first comprehensive framework for leveraging GenAI in transportation planning. Specifically, we introduce a new taxonomy that categorizes existing applications and methodologies into two perspectives: transportation planning tasks and computational techniques. From the transportation planning perspective, we examine the role of GenAI in automating descriptive, predictive, generative simulation, and explainable tasks to enhance mobility systems. From the computational perspective, we detail advancements in data preparation, domain-specific fine-tuning, and inference strategies such as retrieval-augmented generation and zero-shot learning tailored to transportation applications. Additionally, we address critical challenges, including data scarcity, explainability, bias mitigation, and the development of domain-specific evaluation frameworks that align with transportation goals like sustainability, equity, and system efficiency. This survey aims to bridge the gap between traditional transportation planning methodologies and modern AI techniques, fostering collaboration and innovation. By addressing these challenges and opportunities, we seek to inspire future research that ensures ethical, equitable, and impactful use of generative AI in transportation planning…(More)”.
AI-Facilitated Collective Judgements
Article by Manon Revel and Théophile Pénigaud: “This article unpacks the design choices behind longstanding and newly proposed computational frameworks aimed at finding common grounds across collective preferences and examines their potential future impacts, both technically and normatively. It begins by situating AI-assisted preference elicitation within the historical role of opinion polls, emphasizing that preferences are shaped by the decision-making context and are seldom objectively captured. With that caveat in mind, we explore AI-facilitated collective judgment as a discovery tool for fostering reasonable representations of a collective will, sense-making, and agreement-seeking. At the same time, we caution against dangerously misguided uses, such as enabling binding decisions, fostering gradual disempowerment or post-rationalizing political outcomes…(More)”.
Artificial intelligence for digital citizen participation: Design principles for a collective intelligence architecture
Paper by Nicolas Bono Rossello, Anthony Simonofski, and Annick Castiaux: “The challenges posed by digital citizen participation and the amount of data generated by Digital Participation Platforms (DPPs) create an ideal context for the implementation of Artificial Intelligence (AI) solutions. However, current AI solutions in DPPs focus mainly on technical challenges, often neglecting their social impact and not fully exploiting AI’s potential to empower citizens. The goal of this paper is thus to investigate how to design digital participation platforms that integrate technical AI solutions while considering the social context in which they are implemented. Using Collective Intelligence as kernel theory, and through a literature review and a focus group, we generate design principles for the development of a socio-technically aware AI architecture. These principles are then validated by experts from the field of AI and citizen participation. The principles suggest optimizing the alignment of AI solutions with project goals, ensuring their structured integration across multiple levels, enhancing transparency, monitoring AI-driven impacts, dynamically allocating AI actions, empowering users, and balancing cognitive disparities. These principles provide a theoretical basis for future AI-driven artifacts, and theories in digital citizen participation…(More)”.
Bridging the Data Provenance Gap Across Text, Speech and Video
Paper by Shayne Longpre et al: “Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities–popular text, speech, and video datasets–from their detailed sourcing trends and use restrictions to their geographical and linguistic representation. Our manual analysis covers nearly 4000 public datasets between 1990-2024, spanning 608 languages, 798 sources, 659 organizations, and 67 countries. We find that multimodal machine learning applications have overwhelmingly turned to web-crawled, synthetic, and social media platforms, such as YouTube, for their training sets, eclipsing all other sources since 2019. Secondly, tracing the chain of dataset derivations we find that while less than 33% of datasets are restrictively licensed, over 80% of the source content in widely-used text, speech, and video datasets, carry non-commercial restrictions. Finally, counter to the rising number of languages and geographies represented in public AI training datasets, our audit demonstrates measures of relative geographical and multilingual representation have failed to significantly improve their coverage since 2013. We believe the breadth of our audit enables us to empirically examine trends in data sourcing, restrictions, and Western-centricity at an ecosystem-level, and that visibility into these questions are essential to progress in responsible AI. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire multimodal audit, allowing practitioners to trace data provenance across text, speech, and video…(More)”.