Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News
For HCI researchers and news practitioners, it identifies a design problem in AI transparency that undermines trust, offering reader-centered alternatives.
Current AI disclosures in news (brief labels or detailed explanations) fail to build reader trust; detailed disclosures reduce trust (transparency dilemma) while brief ones create information gaps. Readers prefer user-agency designs like detail-on-demand and AI-ratio visualizations.
As newsrooms integrate generative AI, journalists face a disclosure challenge: how to communicate AI involvement in ways that maintain reader trust. Current practice offers two approaches: brief one-line labels or detailed disclosures specifying human oversight, editorial accountability, and error reporting mechanisms. Neither achieves journalists' goal of building trust through transparency. An existing controlled experiment with 34 news readers show that detailed disclosures trigger a \textit{transparency dilemma}, reducing trust rather than increasing it, and risk introducing dark patterns that readers scroll past with the illusion of transparency. One-line disclosures avoid this effect but can create an information gap, prompting readers to expend cognitive effort searching for signs of AI involvement that the disclosure indicates but does not explain. Yet readers are not rejecting transparency, they proposed disclosure designs centered on user agency: detail-on-demand interactions, proportional AI-ratio visualizations, outlet-level signals, and explicit "no AI" labels. I argue that this disconnect between what practitioners believe is responsible disclosure and what users actually need is a design problem for the HCI community.