Mathematics for Safe AI

We don’t yet have known technical solutions to ensure that powerful AI systems interact as intended with real-world systems and populations. A combination of scientific world-models and mathematical proofs may be the answer to ensuring AI provides transformational benefit without harm.

Opportunity seeds


Outside the scope of programmes and with budgets of up to £500k, these opportunity seeds support ambitious research aligned to the Mathematics for Safe AI opportunity space.

We’re funding 15 opportunity seeds exploring different mathematical approaches to help us verify, understand, and control AI systems.

Active

Formalising and Mitigating the ‘Eliciting Latent Knowledge’ Problem

Francis Rhys Ward, Dr Francis Rhys Ward's Research Group

Active

End-to-End Verification for Constraint Programming

Ciaran McCreesh

Active

SFBench

Jason Gross, Theorem + Redwood Research

Active

Hardware-Level AI Safety Verification

Edoardo Manino, University of Manchester; Mirco Giacobbe, University of Birmingham

Active

Dovetail Research

Alfred Harwood, Dovetail Research

Active

FV-Spec: A Large-Scale Benchmark For Formal Verification of Software

Mike Dodds + Ledah Casburn, Galois

Active

Human Inductive Bias Project

Chris Pang, Meridian

Active

Investigate the feasibility of using LLMs to scalably produce Large Logic-based Expert Systems (LLES)

Joar Skalse

Active

Singular Learning Theory for Safe AI Agents

Daniel Murfet, Timaeus

Active

Learning-theoretic AI Alignment Research Agenda

Vanessa Kosoy, CORAL

Active

Combining Physical and Intentional Stance for Safe AI

Martin Biehl, Cross Labs, Cross Compass; Manuel Baltieri, Araya Inc.; Nathaniel Virgo, University of Hertfordshire

Active

SafePlanBench & Logically Constrained Reinforcement Learning

Agustín Martinez Suñé, University of Oxford

Active

GFlowNet-Steered Probabilistic Program Synthesis for Safer AI

Sam Staton, Nikolay Malkin + Younesse Kaddar, University of Oxford

Active

SCRY-AI: Self-operating Calculations of Risk for Yielding Accurate Insights

Vehbi Deger Turan, Metaculus

Active

Extraction of Structured Knowledge from Language for Scientific Discovery

Nikolay Malkin + Henry Gouk, University of Edinburgh

 

This opportunity space is part of our rolling seed call experiment – see what's in scope for opportunity seeds in this space by reading the original call for proposals and apply at the link below.


Learn more and apply