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Showing 201-250 of 459·Sorted by endorsements
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An open, replicable index quantifying how much states depend on foreign AI inference infrastructure, revealing where control over AI is concentrating and what governments can do about it.
How do expert mathematicians think?
A research project designed to reduce existential threats from scenarios where a Sovereign AI proposal with a hidden problem ends up successfully implemented.
Create a course curriculum that covers basic legal, political, sociological, and international relations knowledge relevant to AI Safety. r
A tool that generates RL environments for AI agents and adversarially attacks each one, so models don't train on tasks they can cheat.
Demystifying AI evaluation research by lowering technical barriers through practical, open evaluation infrastructure.
The race to advance the technology has left behind the concerns around harm and risks of such advancement to public health and safety. Whistleblowing in the AI sector bridges such gap to safe and ethical AI.
AI Safety, Ethics, and Education focused TikTok Account for Indonesia, using Indonesia language in casual style.
Findings about models protecting their 'collaborators' against instructions are fragile under framing effects from prompting; investigate a broader range of those effects and how they transfer across models.
Self Improvement through training and field work that seeks to Identify and preventing institutional AI lock-in before it creates durable concentrations of power in democratic governments.
Measuring homogenization and social bias in LLMs and developing interventions to promote diversity.
Short Documentary and Music Video
An open test of whether AI-agent memory systems spread false claims, plus a provenance spec so an agent's memory can be audited, corrected, and forgotten.
Ontoresonant Feedback Loops
Like grandma's recipe card: the receipt proves who wrote it and that nothing changed, not that the cake tastes good. Fund the independent review of that seal.
Measuring whether CoT monitoring fails when an influence reaches an agent through a tool return rather than the user message.
Funds travel and flight-change costs to attend the Human-Aligned AI Summer School after Geneva AI governance events, to strengthen technical grounding for Philippine AI policy and training work.
Building an atlas of concept regions in a language model's hidden space, one geometry that reads, steers, routes, and carves the model's behavior.
A production-proven, system-agnostic framework that enforces correct AI-agent behavior mechanically at the tool-call boundary, where soft rules fail under load.
Build a public dataset and evaluation harness to measure how agent skills/MCP tool scaffolds change model behavior (safety, refusals, unauthorized actions) across popular registries.
Implementing evals for RL and LLM agents' ability to learn and properly apply biologically and economically aligned pluralistic utility functions and with that to avoid runaway conditions.
I'm testing how AI verification improves oversight, when correlated errors make it unreliable and when systems should escalate or abstain.
We are building 'Digital Steel. Kinetic-369 is a deterministic, processor-independent hardware safety kernel designed to prevent embodied AI and critical infrastructure from executing unsafe physical actions in under 200 microsec
A Layered AI Architecture For Grounded Reasoning and Verified Safety
AISafety.com aims to multiply global AI safety efforts through a centralized, comprehensive, and up-to-date resource hub of ‘everything’ AI safety.
An open benchmark and evaluation toolkit for detecting whether shared AI research assistants create correlated blind spots in AI safety-critical research, and for testing workflows that preserve independent reasoning and failure-m
Testing whether activation probes recover safety signals from reasoning models as their chain-of-thought becomes illegible.
A benchmark measuring how often retrieval-grounded models answer confidently when no supporting evidence exists, plus a primitive that turns silent confabulation into an auditable refusal signal.
Providing full-time founder runway to launch NICER: a European institute building the regulatory infrastructure to audit frontier AI agents post-deployment.
LLMs don't retrieve a stable judgment of a person, they reconstruct one to fit how you ask. ObserverBench measures this, because it matters wherever an LLM judges people: hiring, RLHF, agent oversight.
Compiling != faithful: a human-audited benchmark measuring semantic drift in textbook autoformalization — and whether the LLM judges we trust to catch it share the generator's blind spot.
Situation-monitoring project focused on identifying and tracking early indicators of AI-enabled power concentration.
A sealed public registry for AI eval results that lets anyone prove, offline, that no result was rewritten or quietly deleted after publication.
ClauseHound builds a self-hosted privacy-first legal AI node using open-source LLM agents, secure networking, retrieval/search tools, and RL/eval loops to reduce hallucinations and fit law firm workflows.
Building an improved open-source suite to understand multimodal models detect misalignment, for the technically minded community, and communication with this group of audiences
This is the first large-scale study linking AI chatbot conversation logs and clinical records from patients in psychiatric treatment, led by the UCSF AI in Mental Health Research Group.
Does a user's sustained temperament change a model's reliability, efficiency, and alignment-relevant behavior?
An open eval, inspired by MASK (Center for AI Safety), for lies of omission.
An open-source library implementing somatic-marker-style emotional memory for open-weight LLMs — activation-level signals from past outcomes that improve model decision-making.
An independent Australian podcast raising public and policymaker understanding of catastrophic risks from transformative AI — hosted by an ethicist and an AI-governance practitioner in an under-served region for AI safety.
A platform for conditional commitments: pledges to act only when N peers agree, so lab employees, researchers, and policy coalitions can coordinate high-stakes collective action.
We build general artificial agents whose capabilities are shaped by the constraints and frames of reference that shaped human intelligence.
A consumer-GPU study measuring whether open-weight models become better at recognising evaluation contexts as they scale, with a small model-organism experiment testing whether deliberately induced underperformance can be detected
1) Delay: advocate for DNA-synthesis screening and KYC to deny access to pathogens2) Detect: create a pandemic early warning system for novel pathogens3) Defend: stockpile ppe to keep critical workers safe when a pandemic hits
Psychological capture is the soft padded path of gradual disempowerment, Driftwatch is designed to find, name and measure it within frontier models.
Investigating how populations become excluded from AI-relevant health datasets
To create accessible, down to earth educational material that raises public awareness on implications of day to day AI usage, and encourages ethical AI literacy in environments where AI can impact societally changing decisions.
HELGEN builds biosurveillance infrastructure to detect biological threats in the environment on-site, completely automated.
A security solution that blocks AI agents from taking harmful actions
