The Refinement
Problem.

Generative AI makes it easier to produce explanations and arguments at a scale that is difficult to check. Readers still have to decide which claims deserve their confidence.

A fluent, convincing argument can now be produced in seconds. Establishing whether it is sound still requires time, context, and judgment. We must examine the evidence, trace the sources, distinguish observation from inference, and consider what has been left out.

This is the refinement problem: the growing gap between our capacity to produce persuasive information and our capacity to evaluate it. When plausible claims multiply faster than we can examine them, confidence can become easier to generate than to justify.

Checking an answer requires some account of where it came from.

When information is absorbed into systems that do not expose their sources or transformations, the connection between a claim and its evidence becomes harder to reconstruct. An answer may retain the appearance of knowledge while losing the context needed to assess it.

Provenance, the history and origin of information, matters because it makes scrutiny possible. We need to be able to ask who produced a claim, what supports it, how it has changed, and where uncertainty remains.

Shared criteria give readers a starting point for examining an argument. They help identify which claims need closer attention and what evidence would be useful.

Those criteria must themselves be open to scrutiny. Useful standards make their assumptions visible, allow disagreement, and change when better reasoning or evidence warrants revision.

AIR’s mission is to refine the artificial into something more advanced. We build tools that help people check what they are being told and understand the evidence behind it.

That means keeping sources within reach and making uncertainty visible. The person using a system should have enough context to question its answer.