Paper by Rainer Mühlhoff and Hannah Ruschemeier: “The purpose limitation principle goes beyond the protection of the individual data subjects: it aims to ensure transparency, fairness and its exception for privileged purposes. However, in the current reality of powerful AI models, purpose limitation is often impossible to enforce and is thus structurally undermined. This paper addresses a critical regulatory gap in EU digital legislation: the risk of secondary use of trained models and anonymised training datasets. Anonymised training data, as well as AI models trained from this data, pose the threat of being freely reused in potentially harmful contexts such as insurance risk scoring and automated job applicant screening. We propose shifting the focus of purpose limitation from data processing to AI model regulation. This approach mandates that those training AI models define the intended purpose and restrict the use of the model solely to this stated purpose…(More)”.
Technical Tiers: A New Classification Framework for Global AI Workforce Analysis
Report by Siddhi Pal, Catherine Schneider and Ruggero Marino Lazzaroni: “… introduces a novel three-tiered classification system for global AI talent that addresses significant methodological limitations in existing workforce analyses, by distinguishing between different skill categories within the existing AI talent pool. By distinguishing between non-technical roles (Category 0), technical software development (Category 1), and advanced deep learning specialization (Category 2), our framework enables precise examination of AI workforce dynamics at a pivotal moment in global AI policy.
Through our analysis of a sample of 1.6 million individuals in the AI talent pool across 31 countries, we’ve uncovered clear patterns in technical talent distribution that significantly impact Europe’s AI ambitions. Asian nations hold an advantage in specialized AI expertise, with South Korea (27%), Israel (23%), and Japan (20%) maintaining the highest proportions of Category 2 talent. Within Europe, Poland and Germany stand out as leaders in specialized AI talent. This may be connected to their initiatives to attract tech companies and investments in elite research institutions, though further research is needed to confirm these relationships.
Our data also reveals a shifting landscape of global talent flows. Research shows that countries employing points-based immigration systems attract 1.5 times more high-skilled migrants than those using demand-led approaches. This finding takes on new significance in light of recent geopolitical developments affecting scientific research globally. As restrictive policies and funding cuts create uncertainty for researchers in the United States, one of the big destinations for European AI talent, the way nations position their regulatory environments, scientific freedoms, and research infrastructure will increasingly determine their ability to attract and retain specialized AI talent.
The gender analysis in our study illuminates another dimension of competitive advantage. Contrary to the overall AI talent pool, EU countries lead in female representation in highly technical roles (Category 2), occupying seven of the top ten global rankings. Finland, Czechia, and Italy have the highest proportion of female representation in Category 2 roles globally (39%, 31%, and 28%, respectively). This gender diversity represents not merely a social achievement but a potential strategic asset in AI innovation, particularly as global coalitions increasingly emphasize the importance of diverse perspectives in AI development…(More)”
Make privacy policies longer and appoint LLM readers
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)”.
Artificial Intelligence: Generative AI’s Environmental and Human Effects
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)”.
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:
- 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?
- 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.
- 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)”
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)”