Practitioner perspectives on informing decisions in One Health sectors with predictive models

Curated on Posted on by Stefaan Verhulst

Paper by Kim M. Pepin: “Every decision a person makes is based on a model. A model is an idea about how a process works based on previous experience, observation, or other data. Models may not be explicit or stated (Johnson-Laird, 2010), but they serve to simplify a complex world. Models vary dramatically from conceptual (idea) to statistical (mathematical expression relating observed data to an assumed process and/or other data) or analytical/computational (quantitative algorithm describing a process). Predictive models of complex systems describe an understanding of how systems work, often in mathematical or statistical terms, using data, knowledge, and/or expert opinion. They provide means for predicting outcomes of interest, studying different management decision impacts, and quantifying decision risk and uncertainty (Berger et al. 2021; Li et al. 2017). They can help decision-makers assimilate how multiple pieces of information determine an outcome of interest about a complex system (Berger et al. 2021; Hemming et al. 2022).

People rely daily on system-level models to reach objectives. Choosing the fastest route to a destination is one example. Such a decision may be based on either a mental model of the road system developed from previous experience or a traffic prediction mapping application based on mathematical algorithms and current data. Either way, a system-level model has been applied and there is some uncertainty. In contrast, predicting outcomes for new and complex phenomena, such as emerging disease spread, a biological invasion risk (Chen et al. 2023; Elderd et al. 2006; Pepin et al. 2022), or climatic impacts on ecosystems is more uncertain. Here public service decision-makers may turn to mathematical models when expert opinion and experience do not resolve enough uncertainty about decision outcomes. But using models to guide decisions also relies on expert opinion and experience. Also, even technical experts need to make modeling choices regarding model structure and data inputs that have uncertainty (Elderd et al. 2006) and these might not be completely objective decisions (Bedson et al. 2021). Thus, using models for guiding decisions has subjectivity from both the developer and end-user, which can lead to apprehension or lack of trust about using models to inform decisions.

Models may be particularly advantageous to decision-making in One Health sectors, including health of humans, agriculture, wildlife, and the environment (hereafter called One Health sectors) and their interconnectedness (Adisasmito et al. 2022)…(More)”.

AI-enhanced nudging in public policy: why to worry and how to respond

Curated on Posted on by Stefaan Verhulst

Paper by Stefano Calboli & Bart Engelen: “What role can artificial intelligence (AI) play in enhancing public policy nudges and the extent to which these help people achieve their own goals? Can it help mitigate or even overcome the challenges that nudgers face in this respect? This paper discusses how AI-enhanced personalization can help make nudges more means paternalistic and thus more respectful of people’s ends. We explore the potential added value of AI by analyzing to what extent it can, (1) help identify individual preferences and (2) tailor different nudging techniques to different people based on variations in their susceptibility to those techniques. However, we also argue that the successes booked in this respect in the for-profit sector cannot simply be replicated in public policy. While AI can bring benefits to means paternalist public policy nudging, it also has predictable downsides (lower effectiveness compared to the private sector) and risks (graver consequences compared to the private sector). We discuss the practical implications of all this and propose novel strategies that both consumers and regulators can employ to respond to private AI use in nudging with the aim of safeguarding people’s autonomy and agency…(More)”. See also: Engagement Integrity: Ensuring Legitimacy at a time of AI-Augmented Participation

The Global A.I. Divide

Curated on Posted on by Stefaan Verhulst

Article by Adam Satariano and Paul Mozur: “Last month, Sam Altman, the chief executive of the artificial intelligence company OpenAI, donned a helmet, work boots and a luminescent high-visibility vest to visit the construction site of the company’s new data center project in Texas.

Bigger than New York’s Central Park, the estimated $60 billion project, which has its own natural gas plant, will be one of the most powerful computing hubs ever created when completed as soon as next year.

Around the same time as Mr. Altman’s visit to Texas, Nicolás Wolovick, a computer science professor at the National University of Córdoba in Argentina, was running what counts as one of his country’s most advanced A.I. computing hubs. It was in a converted room at the university, where wires snaked between aging A.I. chips and server computers.

“Everything is becoming more split,” Dr. Wolovick said. “We are losing.”

Artificial intelligence has created a new digital divide, fracturing the world between nations with the computing power for building cutting-edge A.I. systems and those without. The split is influencing geopolitics and global economics, creating new dependencies and prompting a desperate rush to not be excluded from a technology race that could reorder economies, drive scientific discovery and change the way that people live and work.

The biggest beneficiaries by far are the United States, China and the European Union. Those regions host more than half of the world’s most powerful data centers, which are used for developing the most complex A.I. systems, according to data compiled by Oxford University researchers. Only 32 countries, or about 16 percent of nations, have these large facilities filled with microchips and computers, giving them what is known in industry parlance as “compute power.”..(More)”.

Library Catalogues as Data: Research, Practice and Usage

Curated on Posted on by Stefaan Verhulst

Book by Paul Gooding, Melissa Terras, and Sarah Ames: “Through the web of library catalogues, library management systems and myriad digital resources, libraries have become repositories not only for physical and digital information resources but also for enormous amounts of data about the interactions between these resources and their users. Bringing together leading practitioners and academic voices, this book considers library catalogue data as a vital research resource.

Divided into four sections, each approaches library catalogues, collections and records from a different angle, from exploring methods for examining such data; to the politics of catalogues and library data; their interdisciplinary potential; and practical uses and applications of catalogues as data. Other topics the volume discusses include:

  • Practical routes to preparing library catalogue data for researchers
  • The ethics of library metadata privacy and reuse
  • Data-driven decision making
  • Data quality and collections bias
  • Preserving, resurrecting and restoring data
  • The uses and potential of historical library data
  • The intersection of catalogue data, AI and Large Language Models (LLMs)

This comprehensive book will be an essential read for practitioners in the GLAM sector, particularly those dealing with collections and catalogue data, and LIS academics and students…(More)”

Misinformation by Omission: The Need for More Environmental Transparency in AI

Curated on Posted on by Stefaan Verhulst

Paper by Sasha Luccioni, Boris Gamazaychikov, Theo Alves da Costa, and Emma Strubell: “In recent years, Artificial Intelligence (AI) models have grown in size and complexity, driving greater demand for computational power and natural resources. In parallel to this trend, transparency around the costs and impacts of these models has decreased, meaning that the users of these technologies have little to no information about their resource demands and subsequent impacts on the environment. Despite this dearth of adequate data, escalating demand for figures quantifying AI’s environmental impacts has led to numerous instances of misinformation evolving from inaccurate or de-contextualized best-effort estimates of greenhouse gas emissions. In this article, we explore pervasive myths and misconceptions shaping public understanding of AI’s environmental impacts, tracing their origins and their spread in both the media and scientific publications. We discuss the importance of data transparency in clarifying misconceptions and mitigating these harms, and conclude with a set of recommendations for how AI developers and policymakers can leverage this information to mitigate negative impacts in the future…(More)”.

The Devil’s Advocate: What Happens When Dissent Becomes Digital

Curated on Posted on by Stefaan Verhulst

Article by Anthea Roberts: “But what if the devil’s advocate wasn’t human at all? What if it was an AI agent—faceless, rank-agnostic, apolitically neutral? A devil without a career to lose. Here’s where the inversion occurs: artificial intelligence enabling more genuine human conversation.

At Dragonfly Thinking, we’ve been experimenting with this concept. We call this Devil’s Advocate your Critical Friend. It’s an AI agent designed to do what humans find personally difficult and professionally dangerous: provide systematic criticism without career consequences.

The magic isn’t in the AI’s intelligence. It’s in how removing the human face transforms the social dynamics of dissent.

When critical feedback comes from an AI, no one’s promotion is at risk. The criticism can be thorough without being insubordinate. Teams can engage with substance rather than navigating office politics.

The AI might note: “Previous digital transformations show 73% failure rate when legacy system dependencies exceed 40%. This proposal shows significant dependencies.” It’s the AI saying what the tech lead knows but can’t safely voice, at least not alone.

Does criticism from code carry less weight because there’s no skin in the game? Counterintuitively, we’ve found the opposite. Without perceived motives or political agendas, the criticism becomes clearer, more digestible.

Ritualizing Productive Dissent

Imagine every major initiative automatically triggering AI analysis. Not optional. Built in like a financial review.

The ritual unfolds:

Monday, 2 PM: The transformation strategy is pitched. Energy builds. Heads nod. The vision is compelling.

Tuesday, 9 AM: An email arrives: “Devil’s Advocate Analysis – Digital Transformation Initiative.” Sender: DA-System. Twelve pages of systematic critique. People read alone, over coffee. Some sections sting. Others confirm private doubts.

Wednesday, 10 AM: The team reconvenes. Printouts are marked up. The tech lead says, “Section 3.2 about integration dependencies—we need to address this.” The ops head adds, “The adoption curve analysis on page 8 matches what we saw in Phoenix.”

Thursday: A revised strategy goes forward. Not perfect, but honest about assumptions and clear about risks.

When criticism is ritualized and automated, it stops being personal. It becomes data…(More)”.

ChatGPT Has Already Polluted the Internet So Badly That It’s Hobbling Future AI Development

Curated on Posted on by Stefaan Verhulst

Article by Frank Landymore: “The rapid rise of ChatGPT — and the cavalcade of competitors’ generative models that followed suit — has polluted the internet with so much useless slop that it’s already kneecapping the development of future AI models.

As the AI-generated data clouds the human creations that these models are so heavily dependent on amalgamating, it becomes inevitable that a greater share of what these so-called intelligences learn from and imitate is itself an ersatz AI creation. 

Repeat this process enough, and AI development begins to resemble a maximalist game of telephone in which not only is the quality of the content being produced diminished, resembling less and less what it’s originally supposed to be replacing, but in which the participants actively become stupider. The industry likes to describe this scenario as AI “model collapse.”

As a consequence, the finite amount of data predating ChatGPT’s rise becomes extremely valuable. In a new feature, The Register likens this to the demand for “low-background steel,” or steel that was produced before the detonation of the first nuclear bombs, starting in July 1945 with the US’s Trinity test. 

Just as the explosion of AI chatbots has irreversibly polluted the internet, so did the detonation of the atom bomb release radionuclides and other particulates that have seeped into virtually all steel produced thereafter. That makes modern metals unsuitable for use in some highly sensitive scientific and medical equipment. And so, what’s old is new: a major source of low-background steel, even today, is WW1 and WW2 era battleships, including a huge naval fleet that was scuttled by German Admiral Ludwig von Reuter in 1919…(More)”.

How to Make Small Beautiful: The Promise of Democratic Innovations

Curated on Posted on by Stefaan Verhulst

Paper by Christoph Niessen & Wouter Veenendaal: “Small states are on average more likely to be democracies and it is often assumed that democracy functions better in small polities. ‘Small is beautiful’, proponents say. Yet, empirical scholarship shows that, while smallness comes with socio-political proximity, which facilitates participation and policy implementation, it also incentivizes personalism, clientelism and power concentration. Largeness, instead, comes with greater socio-political distance, but strengthens institutional checks and entails scale advantages. In this article, we depart from this trade-off and, wondering ‘how to make small beautiful’, we examine a potential remedy: democratic innovations. To do so, we first show that representative institutions were adopted in small polities by replication rather than by choice, and that they can aggravate the democratic problems associated with smallness. Subsequently, we draw on four usages of direct and deliberative democratic practices in small polities to explore which promises they offer to correct some of these pitfalls…(More)”.

National engagement on public trust in data use for single patient record and GP health record published

Curated on Posted on by Stefaan Verhulst

HTN Article: “A large-scale public engagement report commissioned by NHSE on building and maintaining public trust in data use across health and care has been published, focusing on the approach to creating a single patient record and the secondary use of GP data.

It noted “relief” and “enthusiasm” from participants around not having to repeat their health history when interacting with different parts of the health and care system, and highlighted concerns about data accuracy, privacy, and security.

120 participants were recruited for tier one, with 98 remaining by the end, for 15 hours of deliberation over three days in locations including Liverpool, Leicester, Portsmouth, and South London. Inclusive engagement for tier two recruited 76 people from “seldom heard groups” such as those with health needs or socially marginalised groups for interviews and small group sessions. A nationally representative ten-minute online survey with 2,000 people was also carried out in tier three.

“To start with, the concept of a single patient record was met with relief and enthusiasm across Tier 1 and Tier 2 participants,” according to the report….

When it comes to GP data, participants were “largely unaware” of secondary uses, but initially expressed comfort in the idea of it being used for saving lives, improving care, prevention, and efficiency in delivery of services. Concerns were broadly similar to those about the single patient record: concerns about data breaches, incorrect data, misuse, sensitivity of data being shared, bias against individuals, and the potential for re-identification. Some participants felt GP data should be treated differently because “it is likely to contain more intimate information”, offering greater risk to the individual patient if data were to be misused. Others felt it should be included alongside secondary care data to ensure a “comprehensive dataset”.

Participants were “reassured” overall by safeguards in place such as de-identification, staff training in data handling and security, and data regulation such as GDPR and the Data Protection Act. “There was a widespread feeling among Tier 1 and Tier 2 participants that the current model of the GP being the data controller for both direct care and secondary uses placed too much of a burden on GPs when it came to how data is used for secondary purposes,” findings show. “They wanted to see a new model which would allow for greater consistency of approach, transparency, and accountability.” Tier one participants suggested this could be a move to national or regional decision-making on secondary use. Tier three participants who only engaged with the topic online were “more resistant” to moving away from GPs as sole data controllers, with the report stating: “This greater reluctance to change demonstrates the need for careful communication with the public about this topic as changes are made, and continued involvement of the public.”..(More)”.

Government at a Glance 2025

Curated on Posted on by Stefaan Verhulst

OECD Report: “Governments face a highly complex operating environment marked by major demographic, environmental, and digital shifts, alongside low trust and constrained fiscal space. 

Responding effectively means concentrating efforts on three fronts: Enhancing individuals’ sense of dignity in their interactions with government, restoring a sense of security amid rapid societal and economic changes, and improving government efficiency and effectiveness to help boost productivity in the economy, while restoring public finances. These priorities converge in the governance of the green transition.

Government at a Glance 2025 offers evidence-based tools to tackle these long-term challenges…

Governments are not yet making the most of digital tools and data to improve effectiveness and efficiency

Data, digital tools and AI all offer the prospect of efficiency gains. OECD countries score, on average, 0.61 on the Digital Government Index (on a 0-1 scale) but could improve their digital policy frameworks, whole-of-government approaches and use of data as a strategic asset. On average, only 47% of OECD governments’ high-value datasets are openly available, falling to just 37% in education and 42% in health and social welfare…(More)”.