Better human-AI workflows start with better handoffs, not more automation
DrKavner · x · 2026-07-29
This article argues that the most dangerous moment in a human-AI workflow is often when the output looks finished.
It says people are then most likely to stop thinking, even though the model’s summary, recommendation, draft, or ranking may still need scrutiny. The piece connects this to research on:
- automation bias
- algorithm aversion
- algorithm appreciation
Its core point is that better human-AI systems depend on better handoffs—designing workflows so humans stay engaged at the right decision points instead of treating model output as completion.
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