Glossary

Computational reliabilism

Also called: CR

Definition

The view that reliance on opaque computational processes can be warranted by verification, validation, robustness analysis and a track record, without insight into the process.

Computational reliabilism is the view that reliance on an opaque computational process can be warranted by verification and validation methods, robustness analysis and a history of successful implementation, rather than by insight into the process itself. Durán and Formanek developed it in answer to the problem of epistemic opacity in computer simulation: if no one can survey the process, warrant must come from evidence that the process is reliable.

I treat computational reliabilism as an ally rather than a rival. Its proponents have not needed to reclassify opaque computational sources as testifiers to explain why relying on them can be rational, and that is the shape of my proposal for language models: users who learn from them stand to them as competent readers to an instrument, and their warrant is generated by their use.

Burge reached a structurally similar position by another route. Discussing computer proofs, he allows that a recipient who cannot think through a source’s reasoning is reduced to induction over its checked successes, and so to treating the source as an object. Establishing a record under conditions of use and projecting it to the case at hand is the structure of validating an instrument.

What my argument adds is a reason the validation demand is not a formality. For a sound but opaque instrument, validation confirms a connection between output and world that exists whether or not anyone can inspect it. For a system whose production is connected to the truth only through proxies, there is no further connection behind the record to confirm. The record a user projects is all the connection there is.

The picture changes in one place. Where a model’s training signal comes from a deterministic verifier, such as a proof checker or a unit test, production in that domain is regulated by a procedure whose function is to constrain output to truth, and I think that is original satisfaction of the Tracking Minimum. Whether competence acquired under verification transfers to domains where nothing verifies is open, and the burden lies with whoever asserts the transfer.

Questions people also ask

What is computational reliabilism?

It is the view that relying on a computational process you cannot inspect, such as a complex simulation or a trained model, can still be justified, provided its reliability has been established by verification and validation, robustness analysis and a history of successful use.

Who developed computational reliabilism?

Juan M. Durán and Nico Formanek, in a 2018 paper in Minds and Machines, as an answer to Paul Humphreys’s argument that much of computational science is epistemically opaque.

How does computational reliabilism differ from reliabilism?

Process reliabilism is a general theory of justification: a belief is justified if it comes from a reliable process. Computational reliabilism applies that idea to computer methods and says what shows them reliable: indicators such as verification and validation, robustness analysis, a track record of implementations and the knowledge of experts.

Does computational reliabilism apply to large language models?

I think it gives the right shape for relying on them: through evidence of reliability, not insight into the process. My addition is that for systems connected to the truth only through proxies, the validation is not a formality. It is the only connection to the truth the user has.

Questions that turn on it

Further reading

  1. Durán, J. M. and Formanek, N. (2018). Grounds for trust: Essential epistemic opacity and computational reliabilism. Minds and Machines 28(4): 645–666.doi.org/10.1007/s11023-018-9481-6
  2. Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese 169(3): 615–626.doi.org/10.1007/s11229-008-9435-2
  3. Burge, T. (1998). Computer proof, apriori knowledge, and other minds: The sixth Philosophical Perspectives lecture. Philosophical Perspectives 12: 1–37.doi.org/10.1111/0029-4624.32.s12.1