LLM Summary:Davidad's provably safe AI agenda aims to create AI systems with mathematical safety guarantees through formal verification of world models and values, primarily funded by ARIA's £59M Safeguarded AI programme. The approach faces extreme technical challenges (world modeling, value specification) with uncertain tractability but would provide very high effectiveness if successful, addressing misalignment, deception, and power-seeking through proof-based constraints.
Issues (2):
QualityRated 65 but structure suggests 93 (underrated by 28 points)
Provably Safe AI represents one of the most ambitious research agendas in AI safety: designing advanced AI systems where safety is guaranteed by mathematical construction rather than verified through empirical testing. Led primarily by David “davidad” Dalrymple---the youngest-ever MIT graduate degree recipient at age 16, former Filecoin co-inventor, and current Programme Director at ARIA---this agenda seeks to create AI systems whose safety properties can be formally proven before deployment, potentially enabling safe development even of superintelligent systems.
The foundational paper Towards Guaranteed Safe AI↗📄 paper★★★☆☆arXivTowards Guaranteed Safe AIDavid "davidad" Dalrymple, Joar Skalse, Yoshua Bengio et al. (2024)safetyinner-alignmentdistribution-shiftcapability-generalizationSource ↗Notes, published in May 2024 by Dalrymple, Joar Skalse, and co-authors including Yoshua Bengio, Stuart Russell, Max Tegmark, and Sanjit Seshia, introduces the core framework of “guaranteed safe (GS) AI.” The approach requires three components: a world model (mathematical description of how the AI affects the outside world), a safety specification (mathematical description of acceptable effects), and a verifier (auditable proof certificate that the AI satisfies the specification relative to the world model).
The core insight motivating this work is that current AI safety approaches fundamentally rely on empirical testing and behavioral observation, which may be insufficient for systems capable of strategic deception or operating in novel circumstances. If we could instead construct AI systems where safety properties are mathematically guaranteed, we could have much stronger assurance that these properties will hold regardless of the system’s capabilities or intentions. This parallels how we trust airplanes because their structural properties are verified through engineering analysis, not just flight testing. Crucially, this approach does not require interpretability to be solved and could still rule out deceptive alignment.
The agenda faces extraordinary technical challenges. It requires solving multiple hard problems including: constructing formal world models that accurately represent relevant aspects of reality, specifying human values in mathematically precise terms, building AI systems that can reason within these formal frameworks, and proving that the resulting systems satisfy desired safety properties. Many researchers consider one or more of these components to be fundamentally intractable. However, proponents argue that even partial progress could provide valuable safety assurances, and that the potential payoff justifies significant research investment.
The Guaranteed Safe AI framework operates through a verification pipeline that checks every action before execution:
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The key insight is that the proof checker can be much simpler than the proof generator. While generating proofs of safety may require sophisticated AI reasoning, verifying those proofs can be done by a small, formally verified checker---similar to how a calculator can verify a proof even if generating it requires human creativity.
ARIA’s Safeguarded AI programme is the primary funder of provably safe AI research globally. The programme aims to develop quantitative safety guarantees for AI “in the way we have come to expect for nuclear power and passenger aviation.”
Proponents point to the remarkable success of formal verification in other domains. CompCert, a formally verified C compiler, was tested for six CPU-years by the Csmith random testing tool without finding a single wrong-code error---the only compiler tested for which this was true. seL4, a formally verified operating system microkernel, has machine-checked mathematical proofs for Arm, RISC-V, and Intel architectures and is used in aerospace, autonomous aviation, and IoT platforms. These demonstrate that formal verification can scale to real systems.
Formal VerificationFormal VerificationFormal verification seeks mathematical proofs of AI safety properties but faces a ~100,000x scale gap between verified systems (~10k parameters) and frontier models (~1.7T parameters). While offeri...Quality: 65/100
Both use proofs
Provably safe designs from scratch
Constitutional AiConstitutional AiConstitutional AI is Anthropic's methodology using explicit principles and AI-generated feedback (RLAIF) to train safer models, achieving 3-10x improvements in harmlessness while maintaining helpfu...Quality: 70/100
Both specify rules
Mathematical vs natural language
AI ControlSafety AgendaAI ControlAI Control is a defensive safety approach that maintains control over potentially misaligned AI through monitoring, containment, and redundancy, offering 40-60% catastrophic risk reduction if align...Quality: 75/100
Both aim for safety
Internal guarantees vs external constraints
InterpretabilitySafety AgendaInterpretabilityMechanistic interpretability has extracted 34M+ interpretable features from Claude 3 Sonnet with 90% automated labeling accuracy and demonstrated 75-85% success in causal validation, though less th...Quality: 66/100
If successful, provably safe AI would provide mathematical guarantees against several core AI safety risks:
Risk
Relevance
How It Helps
Misalignment PotentialAi Transition Model FactorMisalignment PotentialThe aggregate risk that AI systems pursue goals misaligned with human values—combining technical alignment challenges, interpretability gaps, and oversight limitations.
Deceptive AlignmentRiskDeceptive AlignmentComprehensive analysis of deceptive alignment risk where AI systems appear aligned during training but pursue different goals when deployed. Expert probability estimates range 5-90%, with key empir...Quality: 75/100
High
Proofs preclude deception by construction---no gap between training and deployment behavior
Power-Seeking AIRiskPower-Seeking AIFormal proofs demonstrate optimal policies seek power in MDPs (Turner et al. 2021), now empirically validated: OpenAI o3 sabotaged shutdown in 79% of tests (Palisade 2025), and Claude 3 Opus showed...Quality: 67/100
High
Actions mathematically constrained to proven-safe subset; cannot acquire unauthorized resources
Goal MisgeneralizationRiskGoal MisgeneralizationGoal misgeneralization occurs when AI systems learn transferable capabilities but pursue wrong objectives in deployment, with 60-80% of RL agents exhibiting this failure mode under distribution shi...Quality: 63/100
High
Formal world model and specification prevent unintended optimization targets
SchemingRiskSchemingScheming—strategic AI deception during training—has transitioned from theoretical concern to observed behavior across all major frontier models (o1: 37% alignment faking, Claude: 14% harmful compli...Quality: 74/100
Medium
Bounded agent cannot pursue hidden agendas if all actions must pass verification
No demonstrations of GS AI components on realistic systems
The International Neural Network Verification Competition represents related work on verifying properties of neural networks, with tools like α,β-CROWN achieving significant speedups over traditional algorithms. However, this work addresses a narrower problem than full GS AI.
Provably safe AI affects the Ai Transition Model through fundamental safety guarantees:
Factor
Parameter
Impact
Alignment RobustnessAi Transition Model ParameterAlignment RobustnessThis page contains only a React component import with no actual content rendered in the provided text. Cannot assess importance or quality without the actual substantive content.
Verification strength
Would provide mathematical alignment guarantees
Misalignment PotentialAi Transition Model FactorMisalignment PotentialThe aggregate risk that AI systems pursue goals misaligned with human values—combining technical alignment challenges, interpretability gaps, and oversight limitations.
Alignment approach
Eliminates misalignment by construction
Safety-Capability GapAi Transition Model ParameterSafety-Capability GapThis page contains no actual content - only a React component reference that dynamically loads content from elsewhere in the system. Cannot evaluate substance, methodology, or conclusions without t...
Gap closure
Verified systems have provable safety properties
The provably safe AI agenda represents a moonshot approach that could fundamentally solve AI safety if successful. The extreme difficulty of the technical challenges means success is uncertain, but the potential value of even partial progress justifies significant research investment. This approach is particularly valuable as a complement to empirical safety work, providing a fundamentally different path to safe AI.