AI Governance

Opening — Why this matters now AI governance isn’t just a technical issue—it’s an institutional one. As governments scramble to regulate, corporations experiment with ethics boards, and civil society tries to catch up, the question becomes: who actually holds the power to shape how AI unfolds in the real world? The latest ethnographic study by The Aula Fellowship, Levers of Power in the Field of AI, answers that question not through theory or models, but through people—the policymakers, executives, researchers, and advocates navigating this turbulent terrain. ...

Opening — Why this matters now As Moore’s Law wheezes toward its physical limits, the computing world has shifted its faith from faster cores to more of them. Yet for developers, exploiting this parallelism still feels like assembling IKEA furniture blindfolded — possible, but painful. Enter OMPILOT, a transformer-based model that automates OpenMP parallelization without human prompt engineering, promising to make multicore programming as accessible as autocomplete. ...

Opening — Why this matters now The world’s most powerful language models still speak one tongue: English. From GPT to Claude, most training corpora mirror Silicon Valley’s linguistic hegemony. For smaller nations, this imbalance threatens digital sovereignty — the ability to shape AI in their own cultural and legal terms. Enter PLLuM, the Polish Large Language Model, a national-scale project designed to shift that equilibrium. ...

Opening — Why this matters now In 2025, the world’s enthusiasm for AI regulation has outpaced its understanding of it. Governments publish frameworks faster than models are trained, yet few grasp how these frameworks will sustain relevance as AI systems evolve. The paper “A Taxonomy of AI Regulation Frameworks” argues that the problem is not a lack of oversight, but a lack of memory — our rules forget faster than our models learn. ...

Opening — Why this matters now As large language models (LLMs) weave themselves into every enterprise workflow, a quieter issue looms: the privacy of the data used to prompt them. In‑context learning (ICL) — the art of teaching a model through examples in its prompt — is fast, flexible, and dangerously leaky. Each query could expose confidential examples from private datasets. Enter differential privacy (DP), the mathematical armor for sensitive data — except until now, DP methods for ICL have been clumsy and utility‑poor. ...

Opening — Why this matters now Large language models are learning to cooperate. Or at least, they’re trying. When multiple LLM-driven agents must coordinate—say, to move objects in a shared environment or plan logistics—they often stumble over timing, misunderstanding, and sheer conversational chaos. Each agent talks too much, knows too little, and acts out of sync. DR. WELL, a new neurosymbolic framework from researchers at CMU and USC, proposes a cure: let the agents think symbolically, negotiate briefly, and remember collectively. ...

Why this matters now Large language models have grown remarkably persuasive—but not necessarily reliable. They often arrive at correct answers through logically unsound reasoning, a phenomenon both amusing in games and catastrophic in legal, biomedical, or policy contexts. The research paper VeriCoT: Neuro-Symbolic Chain-of-Thought Validation via Logical Consistency Checks proposes a decisive step toward addressing that flaw: a hybrid system where symbolic logic checks the reasoning of a neural model, not just its answers. ...

Opening — Why this matters now Autonomous research agents have moved from the thought experiment corner of arXiv to its front page. Jr. AI Scientist, a system from the University of Tokyo, represents a quiet but decisive step in that evolution: an AI not only reading and summarizing papers but also improving upon them and submitting its own results for peer (and AI) review. The project’s ambition is as remarkable as its caution—it’s less about replacing scientists and more about probing what happens when science itself becomes partially automated. ...

Opening — Why this matters now The rise of AI in civic life has been faster than most democracies can legislate. Governments and NGOs are experimenting with large language models (LLMs) to summarize public opinions, generate consensus statements, and even draft expert questions in citizen assemblies. The promise? Efficiency and inclusiveness. The risk? Representation by proxy—where the algorithm decides whose questions matter. The new paper Question the Questions: Auditing Representation in Online Deliberative Processes (De et al., 2025) offers a rigorous framework for examining that risk. It turns the abstract ideals of fairness and inclusivity into something measurable, using the mathematics of justified representation (JR) from social choice theory. In doing so, it shows how to audit whether AI-generated “summary questions” in online deliberations truly reflect the people’s diverse concerns—or just the most statistically coherent subset. ...

Opening — Why this matters now When the price of rice in one country spikes, the shock ripples through shipping routes, grain silos, and trade treaties across continents. The global food trade network is as vital as it is volatile—exposed to climate change, geopolitics, and policy oscillations. In 2025, with global food inflation and shipping disruptions returning to headlines, predictive modeling of trade flows has become not just an academic exercise but a policy imperative. ...