AF - Limitations on Formal Verification for AI Safety by Andrew Dickson

The Nonlinear Library

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Limitations on Formal Verification for AI Safety, published by Andrew Dickson on August 19, 2024 on The AI Alignment Forum.
In the past two years there has been increased interest in formal verification-based approaches to AI safety. Formal verification is a sub-field of computer science that studies how guarantees may be derived by deduction on fully-specified rule-sets and symbol systems. By contrast, the real world is a messy place that can rarely be straightforwardly represented in a reductionist way.
In particular, physics, chemistry and biology are all complex sciences which do not have anything like complete symbolic rule sets. Additionally, even if we had such rules for the natural sciences, it would be very difficult for any software system to obtain sufficiently accurate models and data about initial conditions for a prover to succeed in deriving strong guarantees for AI systems operating in the real world.
Practical limitations like these on formal verification have been well-understood for decades to engineers and applied mathematicians building real-world software systems, which makes it puzzling that they have mostly been dismissed by leading researchers advocating for the use of formal verification in AI safety so far.
This paper will focus-in on several such limitations and use them to argue that we should be extremely skeptical of claims that formal verification-based approaches will provide strong guarantees against major AI threats in the near-term.
What do we Mean by Formal Verification for AI Safety?
Some examples of the kinds of threats researchers hope formal verification will help with come from the paper "Provably Safe Systems: The Only Path to Controllable AGI" [1] by Max Tegmark and Steve Omohundro (emphasis mine):
Several groups are working to identify the greatest human existential risks from AGI. For example, the Center for AI Safety recently published 'An Overview of Catastrophic AI Risks' which discusses a wide range of risks including bioterrorism, automated warfare, rogue power seeking AI, etc. Provably safe systems could counteract each of the risks they describe.
These authors describe a concrete bioterrorism scenario in section 2.4: a terrorist group wants to use AGI to release a deadly virus over a highly populated area. They use an AGI to design the DNA and shell of a pathogenic virus and the steps to manufacture it. They hire a chemistry lab to synthesize the DNA and integrate it into the protein shell. They use AGI controlled drones to disperse the virus and social media AGIs to spread their message after the attack.
Today, groups are working on mechanisms to prevent the synthesis of dangerous DNA.
But provably safe infrastructure could stop this kind of attack at every stage: biochemical design AI would not synthesize designs unless they were provably safe for humans, data center GPUs would not execute AI programs unless they were certified safe, chip manufacturing plants would not sell GPUs without provable safety checks, DNA synthesis machines would not operate without a proof of safety, drone control systems would not allow drones to fly without proofs of safety, and armies of
persuasive bots would not be able to manipulate media without proof of humanness.
[1]
The above quote contains a number of very strong claims about the possibility of formally or mathematically provable guarantees around software systems deployed in the physical world - for example, the claim that we could have safety proofs about the real-world good behavior of DNA synthesis machines, or drones.
From a practical standpoint, our default stance towards such claims should be skepticism, since we do not have proofs of this sort for any of the technologies we interact with in the real-world today.
For example, DNA synthesis machines exist today and do no...