Reliance calibration as an output-contingent classification of reliance decisions

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The review defines reliance calibration as "an item-level, output-contingent classification of whether a reliance decision was appropriate given AI-output quality", distinguishing it from trust (a psychological attitude) and reliance (an observable decision). For mixed-quality outputs, calibration may be judged at claim level or on a graded appropriateness scale, because one response can contain accurate, incomplete, and misleading elements simultaneously. Accepting faulty content constitutes over-reliance; rejecting useful content constitutes under-reliance.

Accounts

• Reliance calibration as an output-contingent classification of reliance decisions: The review defines reliance calibration as "an item-level, output-contingent classification of whether a reliance decision was appropriate given AI-output quality", distinguishing it from trust (a psychological attitude) and reliance (an observable decision). For mixed-quality outputs, calibration may be judged at claim level or on a graded appropriateness scale, because one response can contain accurate, incomplete, and misleading elements simultaneously. Accepting faulty content constitutes over-reliance; rejecting useful content constitutes under-reliance. (Wei J et al. (2026))

Claims

• Trust and reliance can diverge, and self-reported reliance measures cannot classify calibration without output-quality ground truth [+W]

Related Research Methods

Key Sources

• Wei J and Shang Y (2026). Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education. Front. Psychol. 17:1965371. https://doi.org/10.3389/fpsyg.2026.1965371

Page metadata

Field	Value
type	research-method
id	reliance-calibration-as-an-output-contingent-classification-of-reliance-decisions
title	Reliance calibration as an output-contingent classification of reliance decisions
description	The review defines reliance calibration as "an item-level, output-contingent classification of whether a reliance decision was appropriate given AI-output quality", distinguishing it from trust (a psychological attitude) and reliance (an observable decision).
status	review
generated	by process:settle-candidates on 2026-10-12
trust	unverified — no one has confirmed this page's content

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