no-one-should-judge-their-own-cause

No one should be judge in their own cause

“A party with a stake in an outcome cannot be trusted to judge it.”

Also: Nemo iudex in causa sua · Don't grade your own homework

Origin

Roman law maxim (nemo iudex in causa sua), a pillar of natural justice in common-law systems. About conflict of interest in people: a judge who benefits from a verdict will, consciously or not, reach it.

Why it should, or should not, apply to models

A model has no stake in the ordinary sense, so if the maxim transfers it is for a different reason: the model that produced an output holds the same context, the same assumptions and the same blind spots that produced the error, so its review re-runs the mistake rather than catching it. That predicts the maxim holds when the reviewer shares the producer's context and weakens when the reviewer has independent grounding (a test result, a verifier, a different model with different context).

Standing: Holds

From reviewed claims only. Unreviewed claims are listed below and marked, and move nothing.

Where it holds only under a condition

Claims that keep the adage but bound it.

In an agent's verify-and-fix loop, deterministic checks — tests, linters, schema validators — should be exhausted before any model-based judge is added, because they are free, fast and give the same verdict every time, while an LLM-as-judge costs tokens per run, returns non-deterministic verdicts, and so belongs only on properties no rule can express, and even then as an advisory signal until measured against human review.
Fixing its own mistakespending review
Where a deterministic check exists it should come first; a model judge is acceptable only for properties no rule can express, and then as an advisory signal. The maxim holds for model judges, less for sensors.

Where it holds

Claims showing the adage applying to models.

Without an external, ground-truth signal — a failing test, a compiler error, a verifier's output — a model's own critique of its reasoning is not a reliable improvement signal, and asking it to review and revise a correct answer often turns it into a wrong one.
Fixing its own mistakes
Without an external signal, a model's own critique is unreliable and revision often turns a correct answer wrong.
The agent that produced an artifact is a biased judge of it — it holds the context and the incentives that skew its assessment — so verification belongs with a deterministic sensor or a separate verifier that reports failures back rather than rewriting the output, and in a multi-agent system the verifier is the one component no agent may override.
Fixing its own mistakespending review
Argued from the mechanism above: the producer holds the context that skewed the output, so verification belongs with a sensor or a separate verifier.

Add a case where it holds or breaks

A break is worth more than a hold. If you have seen this fail for a model, with the setup written down, that is the most useful thing you can add here.