Measuring global AI diffusion remains challenging due to a lack of population-normalized, cross-country usage data. We introduce AI User Share, a novel indicator that estimates the share of each country's working-age population actively using AI tools. Built from anonymized Microsoft telemetry and adjusted for device access and mobile scaling, this metric spans 147 economies and provides consistent, real-time insight into global AI diffusion. We find wide variation in adoption, with a strong correlation between AI User Share and GDP. High uptake is concentrated in developed economies, though usage among internet-connected populations in lower-income countries reveals substantial latent demand. We also detect sharp increases in usage following major product launches, such as DeepSeek in early 2025. While the metric's reliance solely on Microsoft telemetry introduces potential biases related to this user base, it offers an important new lens into how AI is spreading globally. AI User Share enables timely benchmarking that can inform data-driven AI policy.

Since Donald Trump launched his chaotic trade war earlier this year, it has become a truism to say he has plunged the world economy into crisis. At last month's spring meetings of the World Bank and International Monetary Fund in Washington, where policymakers and finance ministers from all over congregated, the attenders were "shellshocked", the economist Eswar Prasad, a former senior IMF official who now teaches at Cornell, told me. "The sense is that the world has changed fundamentally in ways that cannot easily be put back together. Every country has to figure out its own place in this new world order and how to protect its own interests." Trump's assault on the old global order is real. But in taking its measure, it's necessary to look beyond the daily headlines and acknowledge that being in a state of crisis is nothing new to capitalism. It's also important to note that, as Karl Marx wrote in The Eighteenth Brumaire of Louis Napoleon: "Men make their own history, but they do not make it as they please."

Tracking how data is mentioned and used in research papers provides critical insights for improving data discoverability, quality, and production. However, manually identifying and classifying dataset mentions across vast academic literature is resource-intensive and not scalable. This paper presents a machine learning framework that automates dataset mention detection across research domains by leveraging large language models (LLMs), synthetic data, and a two-stage fine-tuning process. We employ zero-shot extraction from research papers, an LLM-as-a-Judge for quality assessment, and a reasoning agent for refinement to generate a weakly supervised synthetic dataset. The Phi-3.5-mini instruct model is pre-fine-tuned on this dataset, followed by fine-tuning on a manually annotated subset. At inference, a ModernBERT-based classifier efficiently filters dataset mentions, reducing computational overhead while maintaining high recall. Evaluated on a held-out manually annotated sample, our fine-tuned model outperforms NuExtract-v1.5 and GLiNER-large-v2.1 in dataset extraction accuracy. Our results highlight how LLM-generated synthetic data can effectively address training data scarcity, improving generalization in low-resource settings. This framework offers a pathway toward scalable monitoring of dataset usage, enhancing transparency, and supporting researchers, funders, and policymakers in identifying data gaps and strengthening data accessibility for informed decision-making.

This study investigates efficient deduplication techniques for a large NLP dataset of economic research paper titles. We explore various pairing methods alongside established distance measures (Levenshtein distance, cosine similarity) and a sBERT model for semantic evaluation. Our findings suggest a potentially low prevalence of duplicates based on the observed semantic similarity across different methods. Further exploration with a human-annotated ground truth set is completed for a more conclusive assessment. The result supports findings from the NLP, LLM based distance metrics.

Bangkok, Thailand – Sheltering from the sun on a street corner, Kridsada Ahjed rues the day he got involved with the loan sharks who now gobble up most of his daily earnings. "I went to the loan sharks because people like me – with no assets or savings – cannot qualify to get help from legitimate banks," Ahjed, a 40-year-old motorcycle taxi driver, told Al Jazeera. "Now almost everything I make in a day goes towards paying the interest on my debt." Kridsada is far from alone. Thailand's household debt reached nearly 87 percent of gross domestic product last year, according to the Bank of Thailand, among the highest on earth.

The AI Act vote passed with an overwhelming majority, and has been heralded as one of the world's most important developments in AI regulation. The European Parliament's president, Roberta Metsola, described it as "legislation that will no doubt be setting the global standard for years to come." Don't hold your breath for any immediate clarity, though. The European system is a bit complicated. Next, members of the European Parliament will have to thrash out details with the Council of the European Union and the EU's executive arm, the European Commission, before the draft rules become legislation.

"The questions asked don't reflect the reality we exist in," says Abdelhamad, a father of two who makes 250 dinars ($353) a month and struggles to make ends meet, as quoted in the report. Takaful also reinforces existing gender-based discrimination by relying on sexist legal codes. The cash assistance is provided to Jordanian citizens only, and one indicator the algorithm takes into account is the size of a household. Although Jordanian men who marry a noncitizen can pass on citizenship to their spouse, Jordanian women who do so cannot. For such women, this results in a lower reportable household size, making them less likely to receive assistance.

But one effort from the US Department of Defense does seem to be effective: xView2. Though it's still in its early phases of deployment, this visual computing project has already helped with disaster logistics and on the ground rescue missions in Turkey. An open-source project that was sponsored and developed by the Pentagon's Defense Innovation Unit and Carnegie Mellon University's Software Engineering Institute in 2019, xView2 has collaborated with many research partners, including Microsoft and the University of California, Berkeley. It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity much faster than is possible with current methods. Ritwik Gupta, the principal AI scientist at the Defense Innovation Unit and a researcher at Berkeley, tells me this means the program can directly help first responders and recovery experts on the ground quickly get an assessment that can aid in finding survivors and help coordinate reconstruction efforts over time.

More than a month has passed since our incredible first-time experience attending the 2022 World Economic Forum Annual Meeting in Davos. The trip to Davos, the people I met, the conversations I had, and the events I attended helped me understand that Citibeats is not a fancy personal project about how to use technology for social good. To me, more than a company (and a great one, thanks to all my colleagues), Citibeats is an idea. And this idea, now more than ever is absolutely necessary. I think there has never been a historical period so lucidly conscious of the change.

Climate change and global migration are at the centre of policymakers' efforts. The World Bank has dedicated an entire report to the topic of water, climate change, and development [1]. Together with the World Bank colleagues we have assembled a new dataset to make climate-induced migration visible with the use of explainable machine learning techniques. We showcase how social data science can help policy to make more informed decisions on some of the largest challenges of our times. Severe storms, rising sea levels, and prolonged droughts; the impact of climate change is real and it is increasingly impacting our life.