Stefaan Verhulst
Paper by Sarah C. Risley, Melissa L. Britsch, Joshua S. Stoll & Heather M. Leslie: “Coastal marine social–ecological systems are experiencing rapid change. Yet, many coastal communities are challenged by incomplete data to inform collaborative research and stewardship. We investigated the role of participatory mapping of local knowledge in addressing these challenges. We used participatory mapping and semi-structured interviews to document local knowledge in two focal social–ecological systems in Maine, USA. By co-producing fine-scale characterizations of coastal marine social–ecological systems, highlighting local questions and needs, and generating locally relevant hypotheses on system change, our research demonstrates how participatory mapping and local knowledge can enhance decision-making capacity in collaborative research and stewardship. The results of this study directly informed a collaborative research project to document changes in multiple shellfish species, shellfish predators, and shellfish harvester behavior and other human activities. This research demonstrates that local knowledge can be a keystone component of collaborative social–ecological systems research and community-lead environmental stewardship…(More)”.
Paper by Przemysław Pałka et al: “In a world of human-only readers, a trade-off persists between comprehensiveness and comprehensibility: only privacy policies too long to be humanly readable can precisely describe the intended data processing. We argue that this trade-off no longer exists where LLMs are able to extract tailored information from clearly-drafted fully-comprehensive privacy policies. To substantiate this claim, we provide a methodology for drafting comprehensive non-ambiguous privacy policies and for querying them using LLMs prompts. Our methodology is tested with an experiment aimed at determining to what extent GPT-4 and Llama2 are able to answer questions regarding the content of privacy policies designed in the format we propose. We further support this claim by analyzing real privacy policies in the chosen market sectors through two experiments (one with legal experts, and another by using LLMs). Based on the success of our experiments, we submit that data protection law should change: it must require controllers to provide clearly drafted, fully comprehensive privacy policies from which data subjects and other actors can extract the needed information, with the help of LLMs…(More)”.
Paper by Rodrigo Ramis-Moyano et al: “The increasing implementation of deliberative mini-publics (DMPs) such as Citizens’ Assemblies and Citizens’ Juries led the OECD to identify a ‘deliberative wave’. The burgeoning scholarship on DMPs has increased understanding of how they operate and their impact, but less attention has been paid to the drivers behind this diffusion. Existing research on democratic innovations has underlined the role of the governing party’s ideology as a relevant variable in the study of the adoption of other procedures such as participatory budgeting, placing left-wing parties as a prominent actor in this process. Unlike this previous literature, we have little understanding of whether mini-publics appeal equally across the ideological spectrum. This paper draws on the large-N OECD database to analyse the impact of governing party affiliation on the commissioning of DMPs in Europe across the last four decades. Our analysis finds the ideological pattern of adoption is less clear cut compared to other democratic innovations such as participatory budgeting. But stronger ideological differentiation emerges when we pay close attention to the design features of DMPs implemented…(More)”.
Book by Jacob Hale Russell and Dennis Patterson: “Experts are not infallible. Treating them as such has done us all a grave disservice and, as The Weaponization of Expertise makes painfully clear, given rise to the very populism that all-knowing experts and their elite coterie decry. Jacob Hale Russell and Dennis Patterson use the devastating example of the COVID-19 pandemic to illustrate their case, revealing how the hubris of all-too-human experts undermined—perhaps irreparably—public faith in elite policymaking. Paradoxically, by turning science into dogmatism, the overweening elite response has also proved deeply corrosive to expertise itself—in effect, doing exactly what elite policymakers accuse their critics of doing.
A much-needed corrective to a dangerous blind faith in expertise, The Weaponization of Expertise identifies a cluster of pathologies that have enveloped many institutions meant to help referee expert knowledge, in particular a disavowal of the doubt, uncertainty, and counterarguments that are crucial to the accumulation of knowledge. At a time when trust in expertise and faith in institutions are most needed and most lacking, this work issues a stark reminder that a crisis of misinformation may well begin at the top…(More)”.
GAO Report: “Generative artificial intelligence (AI) could revolutionize entire industries. In the nearer term, it may dramatically increase productivity and transform daily tasks in many sectors. However, both its benefits and risks, including its environmental and human effects, are unknown or unclear.
Generative AI uses significant energy and water resources, but companies are generally not reporting details of these uses. Most estimates of environmental effects of generative AI technologies have focused on quantifying the energy consumed, and carbon emissions associated with generating that energy, required to train the generative AI model. Estimates of water consumption by generative AI are limited. Generative AI is expected to be a driving force for data center demand, but what portion of data center electricity consumption is related to generative AI is unclear. According to the International Energy Agency, U.S. data center electricity consumption was approximately 4 percent of U.S. electricity demand in 2022 and could be 6 percent of demand in 2026.
While generative AI may bring beneficial effects for people, GAO highlights five risks and challenges that could result in negative human effects on society, culture, and people from generative AI (see figure). For example, unsafe systems may produce outputs that compromise safety, such as inaccurate information, undesirable content, or the enabling of malicious behavior. However, definitive statements about these risks and challenges are difficult to make because generative AI is rapidly evolving, and private developers do not disclose some key technical information.
Selected generative artificial antelligence risks and challenges that could result in human effects
GAO identified policy options to consider that could enhance the benefits or address the challenges of environmental and human effects of generative AI. These policy options identify possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry. In addition, policymakers could choose to maintain the status quo, whereby they would not take additional action beyond current efforts. See below for details on the policy options…(More)”.
Paper by Stefaan Verhulst: “The most consequential failures in data-driven policymaking and AI deployment often stem not from poor models or inadequate datasets but from poorly framed questions. This paper centers question literacy as a critical yet underdeveloped competency in the data and policy landscape. Arguing for a “new science of questions,” it explores what constitutes a good question-one that is not only technically feasible but also ethically grounded, socially legitimate, and aligned with real-world needs. Drawing on insights from The GovLab’s 100 Questions Initiative, the paper develops a taxonomy of question types-descriptive, diagnostic, predictive, and prescriptive-and identifies five essential criteria for question quality: questions must be general yet concrete, co-designed with affected communities and domain experts, purpose-driven and ethically sound, grounded in data and technical realities, and capable of evolving through iterative refinement. The paper also outlines common pathologies of bad questions, such as vague formulation, biased framing, and solution-first thinking. Rather than treating questions as incidental to analysis, it argues for institutionalizing deliberate question design through tools like Q-Labs, question maturity models, and new professional roles for data stewards. Ultimately, the paper contends that the questions are infrastructures of meaning. What we ask shapes not only what data we collect or what models we build but also what values we uphold and what futures we make possible…(More)”.
Essay by Oxford Insights and The Data Tank: “Employing data stewards and embedding responsible data reuse principles in the programme or ecosystem and within participating organisations is one of the pathways forward. Data stewards are proactive agents responsible for catalysing collaboration, tackling these challenges and embedding data reuse practices in their organisations.
The role of Chief Data Officer for government agencies has become more common in recent years and we suggest the same needs to happen with the role of the Chief Data Steward. Chief Data Officers are mostly focused on internal data management and have a technical focus. With the changes in the data governance landscape, this profession needs to be reimagined and iterated. Embedded in both the demand and the supply sides of data, data stewards are proactive agents empowered to create public value by re-using data and data expertise. They are tasked to identify opportunities for productive cross-sectoral collaboration, and proactively request or enable functional access to data, insights, and expertise.
One exception comes from New Zealand. The UN has released a report on the role of data stewards and National Statistical Offices (NSOs) in the new data ecosystem. This report provides many use-cases that can be adopted by governments seeking to establish such a role. In New Zealand, there is an appointed Government Chief Data Steward, who is in charge of setting the strategic direction for government’s data management, and focuses on data reuse altogether.
Data stewards can play an important role in organisations leading data reuse programmes. Data stewards would be responsible for responding to the challenges with participation introduced above.
A Data Steward’s role includes attracting participation for data reuse programmes by:
- Demonstrating and communicating the value proposition of data reuse and collaborations, by engaging in partnerships and steering data reuse and sharing among data commons, cooperatives, or collaborative infrastructures.
- Developing responsible data lifecycle governance, and communicating insights to raise awareness and build trust among stakeholders;
A Data Steward’s role includes maintaining and scaling participation for data reuse programmes by:
- Maintaining trust by engaging with wider stakeholders and establishing clear engagement methodologies. For example, by embedding a social license, data stewards assure the digital self determination principle is embedded in data reuse processes.
- Fostering sustainable partnerships and collaborations around data, via developing business cases for data sharing and reuse, and measuring impact to build the societal case for data collaboration; and
- Innovating in the sector by turning data to decision intelligence to ensure that insights derived from data are more effectively integrated into decision-making processes…(More)”.
Paper by Christopher Walker and Sally Washington: “… presents a process model to guide the production of quality policy advice. The work draws on engagement with both public sector practitioners and academics to design a process model for the development of policy advice that works in practice (can be used by policy professionals in their day-to-day work) and aligns with theory (can be taught as part of explaining the dynamics of a wider policy advisory system). The 5D Model defines five key domains of inquiry: understanding Demand, being open to Discovery, undertaking Design, identifying critical Decision points, and shaping advice to enable Delivery. Our goal is a ‘repeatable, scalable’ model for supporting policy practitioners to provide quality advice to decision makers. The model was developed and tested through an extensive process of engagement with senior policy practitioners who noted the heuristic gave structure to practices that determine how policy advice is organized and formulated. Academic colleagues confirmed the utility of the model for explaining and teaching how policy is designed and delivered within the context of a wider policy advisory system (PAS). A unique aspect of this work was the collaboration and shared interest amongst academics and practitioners to define a model that is ‘useful for teaching’ and ‘useful for doing’…(More)”.
Article by Gabriel Daros: “Brazil’s social security institute, known as INSS, added AI to its app in 2018 in an effort to cut red tape and speed up claims. The office, known for its long lines and wait times, had around 2 million pending requests for everything from doctor’s appointments to sick pay to pensions to retirement benefits at the time. While the AI-powered tool has since helped process thousands of basic claims, it has also rejected requests from hundreds of people like de Brito — who live in remote areas and have little digital literacy — for minor errors.
The government is right to digitize its systems to improve efficiency, but that has come at a cost, Edjane Rodrigues, secretary for social policies at the National Confederation of Workers in Agriculture, told Rest of World.
“If the government adopts this kind of service to speed up benefits for the people, this is good. We are not against it,” she said. But, particularly among farm workers, claims can be complex because of the nature of their work, she said, referring to cases that require additional paperwork, such as when a piece of land is owned by one individual but worked by a group of families. “There are many peculiarities in agriculture, and rural workers are being especially harmed” by the app, according to Rodrigues.
“Each automated decision is based on specified legal criteria, ensuring that the standards set by the social security legislation are respected,” a spokesperson for INSS told Rest of World. “Automation does not work in an arbitrary manner. Instead, it follows clear rules and regulations, mirroring the expected standards applied in conventional analysis.”
Governments across Latin America have been introducing AI to improve their processes. Last year, Argentina began using ChatGPT to draft court rulings, a move that officials said helped cut legal costs and reduce processing times. Costa Rica has partnered with Microsoft to launch an AI tool to optimize tax data collection and check for fraud in digital tax receipts. El Salvador recently set up an AI lab to develop tools for government services.
But while some of these efforts have delivered promising results, experts have raised concerns about the risk of officials with little tech know-how applying these tools with no transparency or workarounds…(More)”.
Article by Jim Fruchterman and Steve Francis: “What happens when a nonprofit program or an entire organization needs to shut down? The communities being served, and often society as a whole, are the losers. What if it were possible to mitigate some of that damage by sharing valuable intellectual property assets of the closing effort for longer term benefit? Organizations in these tough circumstances must give serious thought to a responsible exit for their intangible assets.
At the present moment of unparalleled disruption, the entire nonprofit sector is rethinking everything: language to describe their work, funding sources, partnerships, and even their continued existence. Nonprofit programs and entire charities will be closing, or being merged out of existence. Difficult choices are being made. Who will fill the role of witness and archivist to preserve the knowledge of these organizations, their writings, media, software, and data, for those who carry on, either now or in the future?
We believe leaders in these tough days should consider a model we’re calling Exit to Open (E2O) and related exit concepts to safeguard these assets going forward…
Exit to Open (E2O) exploits three elements:
- We are in an era where the cost of digital preservation is low; storing a few more bytes for a long time is cheap.
- It’s far more effective for an organization’s staff to isolate and archive critical content than an outsider with limited knowledge attempting to do so later.
- These resources are of greatest use if there is a human available to interpret them, and a deliberate archival process allows for the identification of these potential interpreters…(More)”.