No Privacy without AI
Norman Sadeh argues that the rise of autonomous, agentic AI intensifies privacy concerns while simultaneously becoming essential for safeguarding personal data. AI systems now read emails, draft documents, manage calendars, and act on users’ behalf, continuously observing and inferring sensitive attributes. Human users cannot manually track the complexity of modern data practices, making AI‑driven privacy management indispensable. Without AI, effective privacy protection would be practically unattainable in today’s digital ecosystem.
Peace Infrastructures
UN peacekeepers have expanded from traditional security duties to constructing roads, bridges, renewable power plants, and electricity grids in conflict zones. Since the post‑World II era, the Blue Helmets have cemented streets, built bridges, and dug wells, reflecting a broader definition...
AI Summer, Data Winter: What the AI Index Reveals — and What It Doesn’t Yet Measure
The Stanford HAI AI Index Report 2026 paints a picture of an ongoing AI summer, with rapid adoption—over half the global population using AI within three years—record investment, and near‑human performance across many domains. At the same time, the report warns...
Generative AI as a Weapon of War in Iran
On February 28, 2026, a U.S.-Israeli strike dubbed Operation Epic Fury hit Iranian nuclear and military targets, triggering a flood of false media on social platforms. Generative AI tools produced realistic videos, images, and satellite‑style graphics that depicted fictitious explosions,...
How Non-Profits and Governments Use Data to Drive Real System Change
Philanthropic groups are redesigning funding models in the Global South to make data a catalyst for systemic change. Generation India restructured payments, cutting input‑linked fees to 56% and tying 44% to verified job placement and retention, boosting employment outcomes. The...
Same Platform, Different Outcomes: Metadata Practices and Open Data Use
The study examines how metadata design on open‑government data portals influences user behavior across 15 U.S. cities, analyzing 5,863 datasets. Using affordance theory, researchers measured metadata quality and linked it to two usage metrics: dataset views and downloads. Results show...
The Imaginary of Informed Consent: Rethinking Approaches to Data Use for AI in Healthcare
The article examines how India’s Digital Personal Data Protection Act 2023 relies on informed consent to legitimize health data use for AI, but this model struggles with the complex, secondary purposes of AI training. It outlines three consent challenges: multiple...
Data Governance in the AI Era: 10 Shifts Redefining Data, Institutions, and Practice
The essay argues that data governance is the foundation of AI governance, as AI systems depend on high‑quality input data. It outlines ten transformative shifts, including redefined data definitions, expanded ownership, real‑time pipelines, and new ethical risk assessments. These changes...
StatGPT and the Fourth Wave of Open Data
Decades of investment in statistical systems have yielded abundant official data, yet users still struggle to discover, interpret, and apply it. The IMF’s new StatGPT report argues that the core issue is not data availability but (re)usability, highlighting fragmented portals,...
Finding the Innovators Hiding in Plain Sight
Northwestern Innovation Institute launched InnovationInsights, an AI‑driven platform that surfaces university research with high commercial promise. The system creates searchable researcher profiles and assigns each publication a commercial‑potential score based on machine‑learning models trained on decades of data linking academic...
Tipping Out of Trouble: How Societies Transformed and How We Can Do So Again
Marten Scheffer’s new book examines how societies have historically tipped out of crises and offers a scientifically grounded roadmap to avoid ecological and social collapse. Drawing on complex‑systems theory, neuroscience, and case studies—from the abolition of slavery to the end...
Scaling Artificial Intelligence in Health
The OECD released a report outlining a policy checklist to scale artificial intelligence responsibly in health systems. It identifies four pillars—enablers, guardrails, meaningful engagement, and trustworthy deployment—covering nine policy categories and 43 guiding questions. The document highlights persistent barriers such...
Agentic AI and the Next Intelligence Explosion
The article challenges the classic singularity myth, arguing that future AI breakthroughs will be plural and socially embedded rather than a lone super‑intelligence. It draws parallels to past evolutionary transitions, emphasizing that intelligence is relational and high‑dimensional. Two concrete pathways...
A Practical Guide for AI Use by Public Sector Leaders — and Why This Matters Now More Than Ever
Alan Shark’s new book offers a five‑part roadmap for public‑sector leaders to adopt artificial intelligence responsibly. It starts with foundational concepts and five critical questions administrators must ask before deployment. Subsequent sections address governance, auditing, AI fatigue, emerging Chief AI...
More Self-Reflection in Research Can Lead to Better Science
Four new Nature papers assess the reproducibility, replicability, and robustness of social and behavioural science research, drawing on a database of 3,900 papers compiled by the DARPA‑funded SCORE programme. The analysis, involving over 850 researchers, finds that only about half...
