Most AI discussions focus on correctness.

Accuracy. Alignment. Output quality.

But there’s a more fundamental problem underneath all of that:

Who — or what — is actually allowed to execute a decision?

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I just published a paper introducing:

Authority Encoding Risk (AER)

A measurable variable for something most systems don’t track at all:

Authority ambiguity at the moment of execution.

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Today’s systems can tell you:

• if something is likely correct

• if it follows policy

• if it appears safe

But they cannot reliably answer:

Is this decision admissible under real-world authority constraints?

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That gap shows up in:

• automation systems

• AI-assisted decisions

• institutional workflows

• underwriting and loss modeling

And right now, it’s largely invisible.

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The paper breaks down:

• how authority ambiguity propagates into risk

• why existing frameworks fail to capture it

• how it can be measured before loss occurs

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If you’re working anywhere near AI, risk, infrastructure, or decision systems — this is a layer worth paying attention to.

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There’s a category of risk most AI systems don’t even know exists.

This paper represents an initial formulation.

Ongoing work is focused on tightening definitions, expanding evidence, and strengthening the model.

https://papers.ssrn.com/sol3/papers.cfm?abstract\_id=6229278