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)”.
Launch: A Blueprint to Unlock New Data Commons for Artificial Intelligence (AI)
Blueprint by Hannah Chafetz, Andrew J. Zahuranec, and Stefaan Verhulst: “In today’s rapidly evolving AI landscape, it is critical to broaden access to diverse and high-quality data to ensure that AI applications can serve all communities equitably. Yet, we are on the brink of a potential “data winter,” where valuable data assets that could drive public good are increasingly locked away or inaccessible.
Data commons — collaboratively governed ecosystems that enable responsible sharing of diverse datasets across sectors — offer a promising solution. By pooling data under clear standards and shared governance, data commons can unlock the potential of AI for public benefit while ensuring that its development reflects the diversity of experiences and needs across society.
To accelerate the creation of data commons, The Open Data Policy, today, releases “A Blueprint to Unlock New Data Commons for AI” — a guide on how to steward data to create data commons that enable public-interest AI use cases…the document is aimed at supporting libraries, universities, research centers, and other data holders (e.g. governments and nonprofits) through four modules:
- Mapping the Demand and Supply: Understanding why AI systems need data, what data can be made available to train, adapt, or augment AI, and what a viable data commons prototype might look like that incorporates stakeholder needs and values;
- Unlocking Participatory Governance: Co-designing key aspects of the data commons with key stakeholders and documenting these aspects within a formal agreement;
- Building the Commons: Establishing the data commons from a practical perspective and ensure all stakeholders are incentivized to implement it; and
- Assessing and Iterating: Evaluating how the commons is working and iterating as needed.
These modules are further supported by two supplementary taxonomies. “The Taxonomy of Data Types” provides a list of data types that can be valuable for public-interest generative AI use cases. The “Taxonomy of Use Cases” outlines public-interest generative AI applications that can be developed using a data commons approach, along with possible outcomes and stakeholders involved.
A separate set of worksheets can be used to further guide organizations in deploying these tools…(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)”.
Human Development and the Data Revolution
Book edited by Sanna Ojanperä, Eduardo López, and Mark Graham: “…explores the uses of large-scale data in the contexts of development, in particular, what techniques, data sources, and possibilities exist for harnessing large datasets and new online data to address persistent concerns regarding human development, inequality, exclusion, and participation.
Employing a global perspective to explore the latest advances at the intersection of big data analysis and human development, this volume brings together pioneering voices from academia, development practice, civil society organizations, government, and the private sector. With a two-pronged focus on theoretical and practical research on big data and computational approaches in human development, the volume covers such themes as data acquisition, data management, data mining and statistical analysis, network science, visual analytics, and geographic information systems and discusses them in terms of practical applications in development projects and initiatives. Ethical considerations surrounding these topics are visited throughout, highlighting the tradeoffs between benefitting and harming those who are the subjects of these new approaches…(More)”
Standards
Book by Jeffrey Pomerantz and Jason Griffey: “Standards are the DNA of the built environment, encoded in nearly all objects that surround us in the modern world. In Standards, Jeffrey Pomerantz and Jason Griffey provide an essential introduction to this invisible but critical form of infrastructure—the rules and specifications that govern so many elements of the physical and digital environments, from the color of school buses to the shape of shipping containers.
In an approachable, often outright funny fashion, Pomerantz and Griffey explore the nature, function, and effect of standards in everyday life. Using examples of specific standards and contexts in which they are applied—in the realms of technology, economics, sociology, and information science—they illustrate how standards influence the development and scope, and indeed the very range of possibilities of our built and social worlds. Deeply informed and informally written, their work makes a subject generally deemed boring, complex, and fundamentally important comprehensible, clear, and downright engaging…(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)”.
Extending the CARE Principles: managing data for vulnerable communities in wartime and humanitarian crises
Essay by Yana Suchikova & Serhii Nazarovets: “The CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) were developed to ensure ethical stewardship of Indigenous data. However, their adaptability makes them an ideal framework for managing data related to vulnerable populations affected by armed conflicts. This essay explores the application of CARE principles to wartime contexts, with a particular focus on internally displaced persons (IDPs) and civilians living under occupation. These groups face significant risks of data misuse, ranging from privacy violations to targeted repression. By adapting CARE, data governance can prioritize safety, dignity, and empowerment while ensuring that data serves the collective welfare of affected communities. Drawing on examples from Indigenous data governance, open science initiatives, and wartime humanitarian challenges, this essay argues for extending CARE principles beyond their original scope. Such an adaptation highlights CARE’s potential as a universal standard for addressing the ethical complexities of data management in humanitarian crises and conflict-affected environments…(More)”.
Research Handbook on Open Government
Handbook edited by Edited by Mila Gascó-Hernandez, Aryamala Prasad , J. Ramon Gil-Garcia , and Theresa A. Pardo: “In the past decade, open government has received renewed attention. It has increasingly been acknowledged globally as necessary to enhance democratic governance by building on the pillars of transparency, participation, and collaboration (Gil-Garcia et al., 2020). Transnational multistakeholder initiatives, such as the Open Government Partnership, have fostered the development of open government by raising awareness about the concept and encouraging reforms in member countries. In this respect, many countries at the local, state, and federal levels have implemented open government initiatives in different policy domains and government functions, such as procurement, policing, education, and public budgeting. More recently, the emergence of digital technologies to facilitate innovative and collaborative approaches to open government is setting these new efforts apart from previous ones, designed to strengthen information access and transparency. Building a new shared understanding of open government, how various contexts shape the perceptions of open government by different stakeholders, and the ways in which digital technologies can advance open government is important for both research and practice…
the Handbook is structured into five sections, each dedicated to highlighting important facets of open government. Part I delves into the historical evolution of open government, setting the stage for the rest of the Handbook. In Part II, the Handbook presents research on the core components of open government, offering invaluable insights on transparency, participation, and collaboration. Part III focuses on the application of open government across diverse policy domains. Shifting focus, Part IV discusses open government implementation within different geographical and national contexts. Finally, Part V introduces emerging trends in open government research. As a whole, the Handbook offers a comprehensive view of open government, from its origins to its contemporary progress and future trends…(More)”.
Data, waves and wind to be counted in the economy
Article by Robert Cuffe: “Wind and waves are set to be included in calculations of the size of countries’ economies for the first time, as part of changes approved at the United Nations.
Assets like oilfields were already factored in under the rules – last updated in 2008.
This update aims to capture areas that have grown since then, such as the cost of using up natural resources and the value of data.
The changes come into force in 2030, and could mean an increase in estimates of the size of the UK economy making promises to spend a fixed share of the economy on defence or aid more expensive.
The economic value of wind and waves can be estimated from the price of all the energy that can be generated from the turbines in a country.
The update also treats data as an asset in its own right on top of the assets that house it like servers and cables.
Governments use a common rule book for measuring the size of their economies and how they grow over time.
These changes to the rule book are “tweaks, rather than a rewrite”, according to Prof Diane Coyle of the University of Cambridge.
Ben Zaranko of the Institute for Fiscal Studies (IFS) calls it an “accounting” change, rather than a real change. He explains: “We’d be no better off in a material sense, and tax revenues would be no higher.”
But it could make economies look bigger, creating a possible future spending headache for the UK government…(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)”.