Brazil’s AI-powered social security app is wrongly rejecting claims


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)”.

From Answer-Giving to Question-Asking: Inverting the Socratic Method in the Age of AI


Blog by Anthea Roberts: “…If questioning is indeed becoming a premier cognitive skill in the AI age, how should education and professional development evolve? Here are some possibilities:

  1. Assessment Through Iterative Questioning: Rather than evaluating students solely on their answers, we might assess their ability to engage in sustained, productive questioning—their skill at probing, following up, identifying inconsistencies, and refining inquiries over multiple rounds. Can they navigate a complex problem through a series of well-crafted questions? Can they identify when an AI response contains subtle errors or omissions that require further exploration?
  2. Prompt Literacy as Core Curriculum: Just as reading and writing are foundational literacies, the ability to effectively prompt and question AI systems may become a basic skill taught from early education onward. This would include teaching students how to refine queries, test assumptions, and evaluate AI responses critically—recognizing that AI systems still hallucinate, contain biases from their training data, and have uneven performance across different domains.
  3. Socratic AI Interfaces: Future AI interfaces might be designed explicitly to encourage Socratic dialogue rather than one-sided Q&A. Instead of simply answering queries, these systems might respond with clarifying questions of their own: “It sounds like you’re asking about X—can you tell me more about your specific interest in this area?” This would model the kind of iterative exchange that characterizes productive human-human dialogue…(More)”.

How to Survive the A.I. Revolution


Essay by John Cassidy: “It isn’t clear where the term “Luddite” originated. Some accounts trace it to Ned Ludd, a textile worker who reportedly smashed a knitting frame in 1779. Others suggest that it may derive from folk memories of King Ludeca, a ninth-century Anglo-Saxon monarch who died in battle. Whatever the source, many machine breakers identified “General Ludd” as their leader. A couple of weeks after the Rawfolds attack, William Horsfall, another mill owner, was shot dead. A letter sent after Horsfall’s assassination—which hailed “the avenging of the death of the two brave youths who fell at the siege of Rawfolds”—began “By Order of General Ludd.”

The British government, at war with Napoleon, regarded the Luddites as Jacobin insurrectionists and responded with brutal suppression. But this reaction stemmed from a fundamental misinterpretation. Far from being revolutionary, Luddism was a defensive response to the industrial capitalism that was threatening skilled workers’ livelihoods. The Luddites weren’t mindless opponents of technology but had a clear logic to their actions—an essentially conservative one. Since they had no political representation—until 1867, the British voting franchise excluded the vast majority—they concluded that violent protest was their only option. “The burning of Factorys or setting fire to the property of People we know is not right, but Starvation forces Nature to do that which he would not,” one Yorkshire cropper wrote. “We have tried every effort to live by Pawning our Cloaths and Chattles, so we are now on the brink for the last struggle.”

As alarm about artificial intelligence has gone global, so has a fascination with the Luddites. The British podcast “The Ned Ludd Radio Hour” describes itself as “your weekly dose of tech skepticism, cynicism, and absurdism.” Kindred themes are explored in the podcast “This Machine Kills,” co-hosted by the social theorist Jathan Sadowski, whose new book, “The Mechanic and the Luddite,” argues that the fetishization of A.I. and other digital technologies obscures their role in disciplining labor and reinforcing a profit-driven system. “Luddites want technology—the future—to work for all of us,” he told the Guardian.The technology journalist Brian Merchant makes a similar case in “Blood in the Machine: The Origins of the Rebellion Against Big Tech” (2023). Blending a vivid account of the original Luddites with an indictment of contemporary tech giants like Amazon and Uber, Merchant portrays the current wave of automation as part of a centuries-long struggle over labor and power. “Working people are staring down entrepreneurs, tech monopolies, and venture capital firms that are hunting for new forms of labor-saving tech—be it AI, robotics, or software automation—to replace them,” Merchant writes. “They are again faced with losing their jobs to the machine.”..(More)”.

Mind the (Language) Gap: Mapping the Challenges of LLM Development in Low-Resource Language Contexts


White Paper by the Stanford Institute for Human-Centered AI (HAI), the Asia Foundation and the University of Pretoria: “…maps the LLM development landscape for low-resource languages, highlighting challenges, trade-offs, and strategies to increase investment; prioritize cross-disciplinary, community-driven development; and ensure fair data ownership…

  • Large language model (LLM) development suffers from a digital divide: Most major LLMs underperform for non-English—and especially low-resource—languages; are not attuned to relevant cultural contexts; and are not accessible in parts of the Global South.
  • Low-resource languages (such as Swahili or Burmese) face two crucial limitations: a scarcity of labeled and unlabeled language data and poor quality data that is not sufficiently representative of the languages and their sociocultural contexts.
  • To bridge these gaps, researchers and developers are exploring different technical approaches to developing LLMs that better perform for and represent low-resource languages but come with different trade-offs:
    • Massively multilingual models, developed primarily by large U.S.-based firms, aim to improve performance for more languages by including a wider range of (100-plus) languages in their training datasets.
    • Regional multilingual models, developed by academics, governments, and nonprofits in the Global South, use smaller training datasets made up of 10-20 low-resource languages to better cater to and represent a smaller group of languages and cultures.
    • Monolingual or monocultural models, developed by a variety of public and private actors, are trained on or fine-tuned for a single low-resource language and thus tailored to perform well for that language…(More)”

Open with care: transparency and data sharing in civically engaged research


Paper by Ankushi Mitra: “Research transparency and data access are considered increasingly important for advancing research credibility, cumulative learning, and discovery. However, debates persist about how to define and achieve these goals across diverse forms of inquiry. This article intervenes in these debates, arguing that the participants and communities with whom scholars work are active stakeholders in science, and thus have a range of rights, interests, and researcher obligations to them in the practice of transparency and openness. Drawing on civically engaged research and related approaches that advocate for subjects of inquiry to more actively shape its process and share in its benefits, I outline a broader vision of research openness not only as a matter of peer scrutiny among scholars or a top-down exercise in compliance, but rather as a space for engaging and maximizing opportunities for all stakeholders in research. Accordingly, this article provides an ethical and practical framework for broadening transparency, accessibility, and data-sharing and benefit-sharing in research. It promotes movement beyond open science to a more inclusive and socially responsive science anchored in a larger ethical commitment: that the pursuit of knowledge be accountable and its benefits made accessible to the citizens and communities who make it possible…(More)”.

Artificial Intelligence and Big Data


Book edited by Frans L. Leeuw and Michael Bamberger: “…explores how Artificial Intelligence (AI) and Big Data contribute to the evaluation of the rule of law (covering legal arrangements, empirical legal research, law and technology, and international law), and social and economic development programs in both industrialized and developing countries. Issues of ethics and bias in the use of AI are also addressed and indicators of the growth of knowledge in the field are discussed.

Interdisciplinary and international in scope, and bringing together leading academics and practitioners from across the globe, the book explores the applications of AI and big data in Rule of Law and development evaluation, identifies differences in the approaches used in the two fields, and how each could learn from the approaches used in the other, as well as differences in the AI-related issues addressed in industrialized nations compared to those addressed in Africa and Asia.

Artificial Intelligence and Big Data is an essential read for researchers, academics and students working in the fields of Rule of Law and Development, and researchers in institutions working on new applications in AI will all benefit from the book’s practical insights…(More)”.

UAE set to use AI to write laws in world first


Article by Chloe Cornish: “The United Arab Emirates aims to use AI to help write new legislation and review and amend existing laws, in the Gulf state’s most radical attempt to harness a technology into which it has poured billions.

The plan for what state media called “AI-driven regulation” goes further than anything seen elsewhere, AI researchers said, while noting that details were scant. Other governments are trying to use AI to become more efficient, from summarising bills to improving public service delivery, but not to actively suggest changes to current laws by crunching government and legal data.

“This new legislative system, powered by artificial intelligence, will change how we create laws, making the process faster and more precise,” said Sheikh Mohammad bin Rashid Al Maktoum, the Dubai ruler and UAE vice-president, quoted by state media.

Ministers last week approved the creation of a new cabinet unit, the Regulatory Intelligence Office, to oversee the legislative AI push. 

Rony Medaglia, a professor at Copenhagen Business School, said the UAE appeared to have an “underlying ambition to basically turn AI into some sort of co-legislator”, and described the plan as “very bold”.

Abu Dhabi has bet heavily on AI and last year opened a dedicated investment vehicle, MGX, which has backed a $30bn BlackRock AI-infrastructure fund among other investments. MGX has also added an AI observer to its own board.

The UAE plans to use AI to track how laws affect the country’s population and economy by creating a massive database of federal and local laws, together with public sector data such as court judgments and government services.

The AI will “regularly suggest updates to our legislation,” Sheikh Mohammad said, according to state media. The government expects AI to speed up lawmaking by 70 per cent, according to the cabinet meeting readout…(More)”

For sale: Data on US servicemembers — and lots of it


Article by Alfred Ng: “Active-duty members of the U.S. military are vulnerable to having their personal information collected, packaged and sold to overseas companies without any vetting, according to a new report funded by the U.S. Military Academy at West Point.

The report highlights a significant American security risk, according to military officials, lawmakers and the experts who conducted the research, and who say the data available on servicemembers exposes them to blackmail based on their jobs and habits.

It also casts a spotlight on the practices of data brokers, a set of firms that specialize in scraping and packaging people’s digital records such as health conditions and credit ratings.

“It’s really a case of being able to target people based on specific vulnerabilities,” said Maj. Jessica Dawson, a research scientist at the Army Cyber Institute at West Point who initiated the study.

Data brokers gather government files, publicly available information and financial records into packages they can sell to marketers and other interested companies. As the practice has grown into a $214 billion industry, it has raised privacy concerns and come under scrutiny from lawmakers in Congress and state capitals.

Worried it could also present a risk to national security, the U.S. Military Academy at West Point funded the study from Duke University to see how servicemembers’ information might be packaged and sold.

Posing as buyers in the U.S. and Singapore, Duke researchers contacted multiple data-broker firms who listed datasets about active-duty servicemembers for sale. Three agreed and sold datasets to the researchers while two declined, saying the requests came from companies that didn’t meet their verification standards.

In total, the datasets contained information on nearly 30,000 active-duty military personnel. They also purchased a dataset on an additional 5,000 friends and family members of military personnel…(More)”

AI models could help negotiators secure peace deals


The Economist: “In a messy age of grinding wars and multiplying tariffs, negotiators are as busy as the stakes are high. Alliances are shifting and political leaders are adjusting—if not reversing—positions. The resulting tumult is giving even seasoned negotiators trouble keeping up with their superiors back home. Artificial-intelligence (AI) models may be able to lend a hand.

Some such models are already under development. One of the most advanced projects, dubbed Strategic Headwinds, aims to help Western diplomats in talks on Ukraine. Work began during the Biden administration in America, with officials on the White House’s National Security Council (NSC) offering guidance to the Centre for Strategic and International Studies (CSIS), a think-tank in Washington that runs the project. With peace talks under way, CSIS has speeded up its effort. Other outfits are doing similar work.

The CSIS programme is led by a unit called the Futures Lab. This team developed an AI language model using software from Scale AI, a firm based in San Francisco, and unique training data. The lab designed a tabletop strategy game called “Hetman’s Shadow” in which Russia, Ukraine and their allies hammer out deals. Data from 45 experts who played the game were fed into the model. So were media analyses of issues at stake in the Russia-Ukraine war, as well as answers provided by specialists to a questionnaire about the relative values of potential negotiation trade-offs. A database of 374 peace agreements and ceasefires was also poured in.

Thus was born, in late February, the first iteration of the Ukraine-Russia Peace Agreement Simulator. Users enter preferences for outcomes grouped under four rubrics: territory and sovereignty; security arrangements; justice and accountability; and economic conditions. The AI model then cranks out a draft agreement. The software also scores, on a scale of one to ten, the likelihood that each of its components would be satisfactory, negotiable or unacceptable to Russia, Ukraine, America and Europe. The model was provided to government negotiators from those last three territories, but a limited “dashboard” version of the software can be run online by interested members of the public…(More)”.

The New Commons Challenge: Advancing AI for Public Good through Data Commons


Press Release: “The Open Data Policy Lab, a collaboration between The GovLab at New York University and Microsoft, has launched the New Commons Challenge, an initiative to advance the responsible reuse of data for AI-driven solutions that enhance local decision-making and humanitarian response. 

The Challenge will award two winning institutions $100,000 each to develop data commons that fuel responsible AI innovation in these critical areas.

With the increasing use of generative AI in crisis management, disaster preparedness, and local decision-making, access to diverse and high-quality data has never been more essential. 

The New Commons Challenge seeks to support organizations—including start-ups, non-profits, NGOs, universities, libraries, and AI developers—to build shared data ecosystems that improve real-world outcomes, from public health to emergency response.

Bridging Research and Real-World Impact

The New Commons Challenge is about putting data into action,” said Stefaan Verhulst, Co-Founder and Chief Research and Development Officer at The GovLab. “By enabling new models of data stewardship, we aim to support AI applications that save lives, strengthen communities, and enhance local decision-making where it matters most.”

The Challenge builds on the Open Data Policy Lab’s recent report, “Blueprint to Unlock New Data Commons for AI,” which advocates for creating collaboratively governed data ecosystems that support responsible AI development.

How the Challenge Works

The challenge unfolds in two phases: Phase One: Open Call for Concept Notes (April 14 – June 2, 2025) 

Innovators world-wide are invited to submit concept notes outlining their ideas. Phase Two: Full Proposal Submissions & Expert Review (June 2025)

  • Selected applicants will be invited to submit a full proposal
  • An interdisciplinary panel will evaluate proposals based on their impact potential, feasibility, and ethical governance.

Winners Announced in Late Summer 2025

Two selected projects will each receive $100,000 in funding, alongside technical support, mentorship, and global recognition…(More)”.