Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers'Trust

2026-01-14

AI Summary

This study investigates how the granularity of AI disclosure in news content influences reader trust and examines the existence of a “transparency dilemma.” Through a mixed-design experiment manipulating disclosure level (none, one-line, detailed), news type, and AI involvement, complemented by the News Media Trust scale, behavioral measures (source verification and subscription intent), and semi-structured interviews, the research systematically reveals the differential effects of disclosure detail on trust. Findings indicate that only detailed disclosure significantly reduces trust and subscription willingness while increasing source verification behaviors; approximately two-thirds of participants expressed a preference for detailed information. Building on these insights, the study proposes a novel “just-in-time detail” disclosure paradigm that strategically balances transparency with trust preservation.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

As artificial intelligence (AI) is increasingly integrated into news production, calls for transparency about the use of AI have gained considerable traction. Recent studies suggest that AI disclosures can lead to a ``transparency dilemma'', where disclosure reduces readers'trust. However, little is known about how the \textit{level of detail} in AI disclosures influences trust and contributes to this dilemma within the news context. In this 3$\times$2$\times$2 mixed factorial study with 40 participants, we investigate how three levels of AI disclosures (none, one-line, detailed) across two types of news (politics and lifestyle) and two levels of AI involvement (low and high) affect news readers'trust. We measured trust using the News Media Trust questionnaire, along with two decision behaviors: source-checking and subscription decisions. Questionnaire responses and subscription rates showed a decline in trust only for detailed AI disclosures, whereas source-checking behavior increased for both one-line and detailed disclosures, with the effect being more pronounced for detailed disclosures. Insights from semi-structured interviews suggest that source-checking behavior was primarily driven by interest in the topic, followed by trust, whereas trust was the main factor influencing subscription decisions. Around two-thirds of participants expressed a preference for detailed disclosures, while most participants who preferred one-line indicated a need for detail-on-demand disclosure formats. Our findings show that not all AI disclosures lead to a transparency dilemma, but instead reflect a trade-off between readers'desire for more transparency and their trust in AI-assisted news content.

Problem

• AI disclosure
• trust
• news media
• transparency dilemma
• artificial intelligence

Innovation

• AI disclosure
• transparency dilemma
• trust in news
• detail level
• human-AI collaboration