新闻业用AI却难透明，读者更想要可自控的披露方式。
Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News
- 提出用户主导的披露设计：按需查看、比例可视化、媒体级信号和明确‘无AI’标签
- 实验显示详细披露反而降低信任，引发认知负担与信息误读
- 强调需从人机交互设计视角重构AI透明机制，而非仅靠文字说明
随着新闻机构引入生成式AI，记者面临如何有效传达AI参与以维持读者信任的挑战。现有做法分为两种：简短的一行标签或详尽的披露，包括人工监督、编辑责任和错误报告机制。但两者均未达成建立信任的目标。一项针对34名读者的控制实验表明，详尽披露会引发‘透明性困境’，反而降低信任度，并可能引入让读者忽略的‘暗模式’；而一行标签虽避免此问题，却造成信息空白，迫使读者额外费力搜寻AI痕迹。然而，读者并非拒绝透明，而是建议采用基于用户自主权的设计：按需展示细节、按比例呈现AI占比、媒体层面的标识以及明确的‘无AI’标签。因此，当前从业者认为的责任披露与用户实际需求之间的脱节，本质上是一个人机交互（HCI）领域需要解决的设计问题。
原文摘要 · Abstract (English)
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.
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