AI Governance

The “right to be forgotten” (GDPR Art. 17) has always seemed like kryptonite for large language models. Once a trillion-parameter system memorizes personal data, how can it truly be erased without starting training from scratch? Most prior attempts—whether using influence functions or alignment-style fine-tuning—felt like damage control: approximate, unverifiable, and too fragile to withstand regulatory scrutiny. This new paper, Unlearning at Scale, turns the problem on its head. It argues that forgetting is not a mathematical optimization problem, but a systems engineering challenge. If training can be made deterministic and auditable, then unlearning can be handled with the same rigor as database recovery or transaction rollbacks. ...

When you think of AI in public decision-making, you might picture chatbots handling service requests or predictive models flagging infrastructure risks. But what if we let large language models (LLMs) actually allocate resources—acting as digital social planners? That’s exactly what this new study tested, using Participatory Budgeting (PB) both as a practical decision-making task and a dynamic benchmark for LLM reasoning. Why Participatory Budgeting Is the Perfect Testbed PB is more than a budgeting exercise. Citizens propose and vote on projects—parks, public toilets, community centers—and decision-makers choose a subset to fund within a fixed budget. It’s a constrained optimization problem with a human twist: budgets, diverse preferences, and sometimes mutually exclusive projects. ...

When AI Plays Lawmaker: Lessons from NomicLaw’s Multi-Agent Debates Large Language Models are increasingly touted as decision-making aides in policy and governance. But what happens when we let them loose together in a legislative sandbox? NomicLaw — an open-source multi-agent simulation inspired by the self-amending game Nomic — offers a glimpse into how AI agents argue, form alliances, and shape collective rules without human scripts. The Experiment NomicLaw pits LLM agents against legally charged vignettes — from self-driving car collisions to algorithmic discrimination — in a propose → justify → vote loop. Each agent crafts a legal rule, defends it, and votes on a peer’s proposal. Scoring is simple: 10 points for a win, 5 for a tie. Two configurations were tested: ...

The Meek Shall Compute It For the past five years, discussions about AI progress have centered on a simple formula: more data + more compute = better models. This scaling paradigm has produced marvels like GPT-4 and Gemini—but also entrenched a new aristocracy of compute-rich players. Is this inequality here to stay? According to a provocative new paper from MIT CSAIL, the answer may be: not for long. The authors argue that due to the laws of diminishing returns, the performance gap between state-of-the-art (SOTA) models and smaller, cheaper “meek” models will shrink over time. If true, this reframes the future of AI as one not of centralized supremacy, but of widespread, affordable competence. ...

When Ray Kurzweil first proposed the “Law of Accelerating Returns,” he suggested that technological progress builds on itself, speeding up over time. But what if even that framing is too slow? David Orban’s recent paper, Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGI, pushes the discussion into new mathematical territory. Instead of modeling AI progress as exponential (where capability growth accelerates at a constant rate), he proposes something more radical: positive third-order derivatives — or in physics terms, jolts. ...

Machine unlearning, once a fringe technical curiosity, is fast becoming a legal and ethical imperative. With increasing regulatory demands like the GDPR’s “right to be forgotten,” AI developers are being asked a hard question: Can a large language model truly forget? A new paper from researchers at TUM and Mila provides an unexpectedly elegant answer. Instead of fighting model collapse—the phenomenon where iterative finetuning on synthetic data causes a model to forget—they propose embracing it. ...

In a digital world flooded with AI-generated content, the question isn’t if we need to trace origins—it’s how we can do it without breaking everything else. BiMark, a new watermarking framework for large language models (LLMs), may have just offered the first truly practical answer. Let’s unpack why it matters and what makes BiMark different. The Triad of Trade-offs in LLM Watermarking Watermarking AI-generated text is like threading a needle while juggling three balls: ...

🧠 From Freud to Fine-Tuning: What is a Superego for AI? As AI agents gain the ability to plan, act, and adapt in open-ended environments, ensuring they behave in accordance with human expectations becomes an urgent challenge. Traditional approaches like Reinforcement Learning from Human Feedback (RLHF) or static safety filters offer partial solutions, but they falter in complex, multi-jurisdictional, or evolving ethical contexts. Enter the idea of a Superego layer—not a psychoanalytical metaphor, but a modular, programmable conscience that governs AI behavior. Proposed by Nell Watson et al., this approach frames moral reasoning and legal compliance not as traits baked into the LLM itself, but as a runtime overlay—a supervisory mechanism that monitors, evaluates, and modulates outputs according to a predefined value system. ...

As artificial intelligence surges toward superhuman capabilities, one truth becomes unavoidable: the strength of our oversight must grow just as fast as the intelligence of the systems we deploy. Simply hoping that “better AI will supervise even better AI” is not a strategy — it’s wishful thinking. Recent research from MIT and collaborators proposes a bold new way to think about this challenge: Nested Scalable Oversight (NSO) — a method to recursively layer weaker systems to oversee stronger ones1. One of the key contributors, Max Tegmark, is a physicist and cosmologist at MIT renowned for his work on AI safety, the mathematical structure of reality, and existential risk analysis. Tegmark is also the founder of the Future of Life Institute, an organization dedicated to mitigating risks from transformative technologies. ...

Introduction In the race to develop increasingly powerful AI agents, it is tempting to believe that size and scale alone will determine success. OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini are all remarkable examples of cutting-edge large language models (LLMs) capable of handling complex, end-to-end tasks. But behind the marvel lies a critical commercial reality: these models are not free. For enterprise applications, the cost of inference can become a serious bottleneck. As firms aim to deploy AI across workflows, queries, and business logic, every API call adds up. This is where a more deliberate, resourceful approach can offer not just a competitive edge—but a sustainable business model. ...