Stefaan Verhulst
Report by the Observatory on Information and Democracy: “This inaugural meta-analysis provides a critical assessment of the role of information ecosystems in the Global North and Global Majority World, focusing on their relationship with information integrity (the quality of public discourse), the fairness of political processes, the protection of media freedoms, and the resilience of public institutions.
The report addresses three thematic areas with a cross-cutting theme of mis- and disinformation:
- Media, Politics and Trust;
- Artificial Intelligence, Information Ecosystems and Democracy;
- and Data Governance and Democracy.
The analysis is based mainly on academic publications supplemented by reports and other materials from different disciplines and regions (1,664 citations selected among a total corpus of over +2700 resources aggregated). The report showcases what we can learn from landmark research on often intractable challenges posed by rapid changes in information and communication spaces…(More)”.
US Department of Commerce: “…This guidance provides actionable guidelines and best practices for publishing open data optimized for generative AI systems. While it is designed for use by the Department of Commerce and its bureaus, this guidance has been made publicly available to benefit open data publishers globally…(More)”. See also: A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI
Essay by Fergus McIntosh: “…For journalists, as for anyone, there are certain shortcuts to trustworthiness, including reputation, expertise, and transparency—the sharing of sources, for example, or the prompt correction of errors. Some of these shortcuts are more perilous than others. Various outfits, positioning themselves as neutral guides to the marketplace of ideas, now tout evaluations of news organizations’ trustworthiness, but relying on these requires trusting in the quality and objectivity of the evaluation. Official data is often taken at face value, but numbers can conceal motives: think of the dispute over how to count casualties in recent conflicts. Governments, meanwhile, may use their powers over information to suppress unfavorable narratives: laws originally aimed at misinformation, many enacted during the COVID-19 pandemic, can hinder free expression. The spectre of this phenomenon is fuelling a growing backlash in America and elsewhere.
Although some categories of information may come to be considered inherently trustworthy, these, too, are in flux. For decades, the technical difficulty of editing photographs and videos allowed them to be treated, by most people, as essentially incontrovertible. With the advent of A.I.-based editing software, footage and imagery have swiftly become much harder to credit. Similar tools are already used to spoof voices based on only seconds of recorded audio. For anyone, this might manifest in scams (your grandmother calls, but it’s not Grandma on the other end), but for a journalist it also puts source calls into question. Technologies of deception tend to be accompanied by ones of detection or verification—a battery of companies, for example, already promise that they can spot A.I.-manipulated imagery—but they’re often locked in an arms race, and they never achieve total accuracy. Though chatbots and A.I.-enabled search engines promise to help us with research (when a colleague “interviewed” ChatGPT, it told him, “I aim to provide information that is as neutral and unbiased as possible”), their inability to provide sourcing, and their tendency to hallucinate, looks more like a shortcut to nowhere, at least for now. The resulting problems extend far beyond media: election campaigns, in which subtle impressions can lead to big differences in voting behavior, feel increasingly vulnerable to deepfakes and other manipulations by inscrutable algorithms. Like everyone else, journalists have only just begun to grapple with the implications.
In such circumstances, it becomes difficult to know what is true, and, consequently, to make decisions. Good journalism offers a way through, but only if readers are willing to follow: trust and naïveté can feel uncomfortably close. Gaining and holding that trust is hard. But failure—the end point of the story of generational decay, of gold exchanged for dross—is not inevitable. Fact checking of the sort practiced at The New Yorker is highly specific and resource-intensive, and it’s only one potential solution. But any solution must acknowledge the messiness of truth, the requirements of attention, the way we squint to see more clearly. It must tell you to say what you mean, and know that you mean it…(More)”.
Essay by Iqbal Dhaliwal: “Artificial intelligence (AI) has the potential to transform our lives. Like the internet, it’s a general-purpose technology that spans sectors, is widely accessible, has a low marginal cost of adding users, and is constantly improving. Tech companies are rapidly deploying more capable AI models that are seeping into our personal lives and work.
AI is also swiftly penetrating the social sector. Governments, social enterprises, and NGOs are infusing AI into programs, while public treasuries and donors are working hard to understand where to invest. For example, AI is being deployed to improve health diagnostics, map flood-prone areas for better relief targeting, grade students’ essays to free up teachers’ time for student interaction, assist governments in detecting tax fraud, and enable agricultural extension workers to customize advice.
But the social sector is also rife with examples over the past two decades of technologies touted as silver bullets that fell short of expectations, including One Laptop Per Child, SMS reminders to take medication, and smokeless stoves to reduce indoor air pollution. To avoid a similar fate, AI-infused programs must incorporate insights from years of evidence generated by rigorous impact evaluations and be scaled in an informed way through concurrent evaluations.
Specifically, implementers of such programs must pay attention to three elements. First, they must use research insights on where AI is likely to have the greatest social impact. Decades of research using randomized controlled trials and other exacting empirical work provide us with insights across sectors on where and how AI can play the most effective role in social programs.
Second, they must incorporate research lessons on how to effectively infuse AI into existing social programs. We have decades of research on when and why technologies succeed or fail in the social sector that can help guide AI adopters (governments, social enterprises, NGOs), tech companies, and donors to avoid pitfalls and design effective programs that work in the field.
Third, we must promote the rigorous evaluation of AI in the social sector so that we disseminate trustworthy information about what works and what does not. We must motivate adopters, tech companies, and donors to conduct independent, rigorous, concurrent impact evaluations of promising AI applications across social sectors (including impact on workers themselves); draw insights emerging across multiple studies; and disseminate those insights widely so that the benefits of AI can be maximized and its harms understood and minimized. Taking these steps can also help build trust in AI among social sector players and program participants more broadly…(More)”.
Essay by Nick Romeo: “Last July, an unusual letter arrived at Kathryn Kundmueller’s mobile home, in central Oregon. It invited her to enter a lottery that would select thirty residents of Deschutes County to deliberate for five days on youth homelessness—a visible and contentious issue in an area where the population and cost of living have spiked in recent years. Those chosen would be paid for their time—almost five hundred dollars—and asked to develop specific policy recommendations.
Kundmueller was being invited to join what is known as a citizens’ assembly. These gatherings do what most democracies only pretend to: trust normal people to make decisions on difficult policy questions. Many citizens’ assemblies follow a basic template. They impanel a random but representative cross-section of a population, give them high-quality information on a topic, and ask them to work together to reach a decision. In Europe, such groups have helped spur reform of the Irish constitution in order to legalize abortion, guided an Austrian pharmaceutical heiress on how to give away her wealth, and become a regular part of government in Paris and Belgium. Though still rare in America, the model reflects the striking idea that fundamental problems of politics—polarization, apathy, manipulation by special interests—can be transformed through radically direct democracy.
Kundmueller, who is generally frustrated by politics, was intrigued by the letter. She liked the prospect of helping to shape local policy, and the topic of housing insecurity had a particular resonance for her. As a teen-ager, following a falling-out with her father, she spent months bouncing between friends’ couches in Vermont. When she moved across the country to San Jose, after college, she lived in her car for a time while she searched for a stable job. She worked in finance but became disillusioned; now in her early forties, she ran a small housecleaning business. She still thought about living in a van and renting out her mobile home to save money…(More)”.
Article by Andrew Schroeder: “One of the most important factors for humanitarian responders in these types of large-scale disaster situations is to understand the effects on the formal health system, upon which most people — and vulnerable communities in particular — rely upon in their neighborhoods. Evaluation of the impact of disasters on individual structures, including critical infrastructure such as health facilities, is traditionally a relatively slow and manually arduous process, involving extensive ground truth visitation by teams of assessment professionals.
Speeding up this process without losing accuracy, while potentially improving the safety and efficiency of assessment teams, is among the more important analytical efforts Direct Relief can undertake for response and recovery efforts. Manual assessments can now be effectively paired with AI-based analysis of satellite imagery to do just that…
With the advent of geospatial AI models trained on disaster damage impacts, ground assessment is not the only tool available to response agencies and others seeking to understand how much damage has occurred and the degree to which that damage may affect essential services for communities. The work of the Oregon State University team of experts in remote sensing-based post-disaster damage detection, led by Jamon Van Den Hoek and Corey Scher, was featured in the Financial Times on January 9.
Their modeling, based on Sentinel-1 satellite imagery, identified 21,757 structures overall, of which 11,124 were determined to have some level of damage. The Oregon State model does not distinguish between different levels of damage, and therefore cannot respond to certain types of questions that the manual inspections can respond to, but nevertheless the coverage area and the speed of detection have been much greater…(More)”.
Paper by Lydia Jennings et al: “In the age of big data and open science, what processes are needed to follow open science protocols while upholding Indigenous Peoples’ rights? The Earth Data Relations Working Group (EDRWG), convened to address this question and envision a research landscape that acknowledges the legacy of extractive practices and embraces new norms across Earth science institutions and open science research. Using the National Ecological Observatory Network (NEON) as an example, the EDRWG recommends actions, applicable across all phases of the data lifecycle, that recognize the sovereign rights of Indigenous Peoples and support better research across all Earth Sciences…(More)”
Paper by Jake Lever et al: “The rapid growth of data-driven applications is ubiquitous across virtually all scientific domains, and has led to an increasing demand for effective methods to handle data deficiencies and mitigate the effects of imperfect data. This paper presents a guide for researchers encountering real-world data-driven applications, and the respective challenges associated with this. This article proposes the concept of the Data Learning Paradigm, combining the principles of machine learning, data science and data assimilation to tackle real-world challenges in data-driven applications. Models are a product of the data upon which they are trained, and no data collected from real world scenarios is perfect due to natural limitations of sensing and collection. Thus, computational modelling of real world systems is intrinsically limited by the various deficiencies encountered in real data. The Data Learning Paradigm aims to leverage the strengths of data improvement to enhance the accuracy, reliability, and interpretability of data-driven models. We outline a range of methods which are currently being implemented in the field of Data Learning involving machine learning and data science methods, and discuss how these mitigate the various problems associated with data-driven models, illustrating improved results in a multitude of real world applications. We highlight examples where these methods have led to significant advancements in fields such as environmental monitoring, planetary exploration, healthcare analytics, linguistic analysis, social networks, and smart manufacturing. We offer a guide to how these methods may be implemented to deal with general types of limitations in data, alongside their current and potential applications…(More)”.
Introduction to the Journal of Sortition: “Since ancient times sortition (random selection by lot) has been used both to distribute political office and as a general prophylactic against factionalism and corruption in societies as diverse as classical-era Athens and the Most Serene Republic of Venice. Lotteries have also been employed for the allocation of scarce goods such as social housing and school places to eliminate bias and ensure just distribution, along with drawing lots in circumstances where unpopular tasks or tragic choices are involved (as some situations are beyond rational human decision-making). More recently, developments in public opinion polling using random sampling have led to the proliferation of citizens’ assemblies selected by lot. Some activists have even proposed such bodies as an alternative to elected representatives. The Journal of Sortition benefits from an editorial board with a wide range of expertise and perspectives in this area. In this introduction to the first issue, we have invited our editors to explain why they are interested in sortition, and to outline the benefits (and pitfalls) of the recent explosion of interest in the topic…(More)”.
Paper by Josie Wittmer, Carolyn Prouse, and Mohammed Rafi Arefin: “Expanded during the COVID-19 pandemic, Wastewater-Based Surveillance (WBS) is now heralded by scientists and policy makers alike as the future of monitoring and governing urban health. The expansion of WBS reflects larger neoliberal governance trends whereby digitalizing states increasingly rely on producing big data as a ‘best practice’ to surveil various aspects of everyday life. With a focus on three South Asian cities, our paper investigates the transnational pathways through which WBS data is produced, made known, and operationalized in ‘evidence-based’ decision-making in a time of crisis. We argue that in South Asia, wastewater surveillance data is actively produced through fragile but power-laden networks of transnational and local knowledge, funding, and practices. Using mixed qualitative methods, we found these networks produced artifacts like dashboards to communicate data to the public in ways that enabled claims to objectivity, ethical interventions, and transparency. Interrogating these representations, we demonstrate how these artifacts open up messy spaces of translation that trouble linear notions of objective data informing accountable, transparent, and evidence-based decision-making for diverse urban actors. By thinking through the production of precarious biosurveillance infrastructures, we respond to calls for more robust ethical and legal frameworks for the field and suggest that the fragility of WBS infrastructures has important implications for the long-term trajectories of urban public health governance in the global South…(More)”