Why we punish people who disclose AI use

Plus every AI prompt I used to write this, published in full. The research on effort, trust and detection.

The conversation about how much AI is too much AI in your artisanal, slow-roast, small-batch media diet has really kicked up a notch lately.

On July 21, Substack went into a bit of a meltdown after the platform turned on AI detection software Pangram — much to the displeasure of many of the platform’s writers. Then on July 30, LinkedIn announced it was going to start rolling out a “seems like AI slop” button, and replaced its “enhance your post” AI assistant with a simple proofreader.

And of course all this week beloved science communicator Hank Green was kind of self-immolating after admitting he used ChatGPT to prep his episodes and suggesting his relationship to AI was “not healthy”.

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You promised me prompts, Rachael. Where the hell are they?
(Scroll to the bottom of this newsletter or if you can’t wait, click here.)

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Why do we like things more when they're hard … for other people?

And look, for a while now the data has shown people really do care about this stuff. Actually that was true well before the recent advent of The Machine — and in far more ways than might be immediately apparent.

In this 2004 study, for example, researchers found people assessed an artistic product — a poem, painting or suit of armour — as higher quality, and even said they liked it more, depending on how much time and effort they thought it took to produce.

And when others do things the hard way, we consider that not only higher quality but morally superior. A paper published in the journal Scientific Reports in April showed test subjects thought negatively of people who were described as having lost weight using a GLP-1 compared with people who lost weight the old-fashioned way. Even weight loss is valued more highly and assessed as being of better moral quality if it is harder to achieve.

We like things to be hard — at least for other people.

For example, an article published in the Journal of Experimental Psychology: General in 2016 found that the level of effort involved in producing an outcome intensified how we felt about the action, good or bad. Essentially: if you mean well and put a lot of work into something, people think well of you, regardless of whether you were successful; if you have bad intentions, and put in a lot of work, people also think worse of you, regardless of outcome. Later researchers call this “effort moralisation”. And that “regardless of outcome” is an important distinction. The thing people valued wasn’t the quality of the result or output, it was the implied care inherent in the effort.

At the same time, studies consistently show that people — and other organisms — instinctively avoid hard work. In the literature it’s often called “Hull’s law of least work”: given a choice between similarly rewarding options, creatures learn to take the easier path.

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Research finds you can, of course, incentivise effort expenditure in exchange for a reward, but even then people tend to make a subconscious calculation about how valuable the reward is and discount it against the level of anticipated effort. We really prefer not to do hard stuff if we can.

When we do do hard things, we later judge the outcome as more valuable. Not while we are doing it mind you, usually only after the fact. One version of this is the IKEA effect (really top-notch naming there, I bet those guys are fun): building the flatpack yourself means you come to feel the furniture is of higher value than a piece built by someone else.

And how people feel about how other people use AI is no different.

Why being honest about AI backfires

Siavosh Sahebi is a PhD candidate at Macquarie University and coauthor of this recent paper on AI and trust.

That research found a few things:

1. Both on social media and in work, people think stuff made with AI is “less trustworthy, less authentic, and less useful”; and

2. People say they wouldn’t judge AI use if it’s disclosed, but they are wrong.

“We asked participants whether they think that disclosing the use of AI is important, a significant portion said yes,” Sahebi tells me. But although the majority of participants said they would not penalise AI use if it was properly disclosed, “our findings show that they do, in fact, penalise AI use”

This presents a conundrum for AI disclosure, because we do want disclosure to take place. It’s super important for accountability and accuracy reasons. And as Sahebi points out, AI is really being shoved down our throats right now. It’s not like we can completely avoid it, even if we wanted to. So we need people to disclose their use without penalising that honest disclosure.

But exactly how we reach a reasonable level of transparency is still up for debate.

A pre-print published in January from researchers in the Netherlands ran into the disclosure dilemma Sahebi outlines hard. In a study of preferences around news a majority of readers said they would prefer a detailed explanation of how AI was used. But in reality, no disclosure or a one-liner had no impact on readership or subscriptions, whereas a detailed disclosure about AI use reduced trust in news and subs took a hit.

How accurate is Pangram's AI detection?

If you read the laments of writers on social media, you’ll see a lot of claims that Pangram is doing a lousy job. (I found Dr Sam Illingworth’s writing about it pretty persuasive.)

Meanwhile in June the journal International Journal for Educational Integrity published a paper that suggests unlike previous iterations of AI detection software, Pangram was pretty damn accurate. I will say I found this study a little methodologically challenged, given their control was 40 graduate articles written before 2019.

The issues with that, as I see it, are twofold:

First, LLMs were trained on the stylistic conventions of academic papers as well as social media and news writing — a lot of the alleged hallmarks of LLM use are things like the em dash, which, as a professional writer, you can pry out of my cold dead hands along with my millennial skinny jeans. News writing was not part of the control group. (That said, pre-review research by the American National Bureau of Economic Research released last year did test on pre-2020 news writing and found false positives were also near zero.)

And second, perhaps more importantly, we know that language is memetic.

Is AI changing the way humans communicate?

As people consume more and more AI-mediated language themselves, some AI-isms are likely to drift into their own writing. A pre-print published in July by a mix of European researchers found that “machines trained on human data now feed their own traits back into human language”.

They ran an experiment where they tried to see if an interaction with an AI chatbot could incept the participants with particular word choices. It did. Fast. Like we’re talking one short conversation. And people thought the new word choice was their own.

The paper flags the emergent risk that consistent, widespread AI use could lead to the homogenisation of language and culture over time, and the potential of a handful of AI companies to manipulate or influence thoughts and beliefs at scale.

But can it really change your mind?

We already know that LLMs are extremely persuasive; I like the term used by the University of Sydney Business School’s Doctors Sandra Peter and Kai Riemer — “seductive”.

In 2025 the American academic Elizabeth Loftus, who has spent half a century researching the creation of false memories, teamed up with researchers at MIT to see if they could use AI to do it even more effectively. Unfortunately the answer was, yes, absolutely.

The AI didn’t just help implant new memories, it made people question their own true memories.

So you might not even know that your writing or speech or even thoughts and ideas are being subtly influenced by what you read in an LLM. And we’re also bad at intuiting what was created with AI — hence the demand for the detection tools.

And it does matter, even beyond our sense of outraged value. We don’t like being manipulated, and we’re currently living in a world with substantially degraded levels of trust in the media, public institutions and our own lying eyes on social media and more generally online.

In this 2025 paper, also coauthored by Sahebi, they unpack the central challenge that the AI slopification of our information environment has wrought. The authors argue we’re left with two options:

1. Keep trusting as normal and risk getting duped and manipulated.

2. Never trust anything, and sometimes wrongly discredit people who’ve done nothing wrong.

And that’s a dark turn of events.

As Sahebi says: “If we adopt a generally distrustful disposition, then we might end up causing what we call ‘epistemic injustice’ towards people, unjustifiably. So, for example, somebody who hasn’t used AI could be wrongly accused and then that person’s credibility is completely shot.”

(“Epistemic injustice” is essentially an unfairness in the sharing or creation of knowledge. Sahebi uses it in this case to refer to unfair damage to a person’s credibility and status as an authority. My extrapolation: we’re potentially accusing someone of an effort crime or intellectual stolen valour when the reality of whether the allegation of AI use is true is not really known or knowable to us.)

Or people may have used AI in completely legitimate and fair ways, to improve their grammar as non-native speakers, for example, and are then penalised unfairly.

“It can be weaponised in that way,” he says.

The solution, it would seem, is not going to be a technological one but a cultural one. I’d argue we need a reshaping of agreed cultural norms around what “acceptable” AI use — and disclosure — looks like.

To that end: an exercise in radical transparency

I’m going to do the thing the Dutch researchers I referenced earlier said was bad. I’m going to give you the most detailed explanation of how I use AI. You tell me how you feel about it when you’ve seen it.

Here are all of the AI skills I used in the production of this newsletter and exactly how I used them. Feel free to interrogate them at your leisure — or appropriate them for your own use.

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If this letter gave you something to think about, consider forwarding it to one person who’d enjoy it too! Word of mouth is how a newsletter this new finds its people.

And I’d love to hear from you — reply to this email, comment in the app, or find me on LinkedIn or Bluesky.

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1. Research Mode

I mainly use Claude Pro. All these skills are my current versions as loaded in Claude with specific references to the names of my projects etc removed, so they’re fit for your personal use.

Research Mode is what I use instead of Google.

In my opinion Google Search is officially borked and you kind of have to use an LLM to surface quality results these days. Feel free to debate me on that one.

I will ask it things like: “I am writing an article about all the recent AI disclosure controversy. Find me canonical sources to construct an accurate timeline of recent developments including the Substack Pangram launch, LinkedIn changes, and the Hank Green incident.” Or: “I remember reading an article where MIT researchers used AI to create false memories. Find that.”

I always read the actual articles — you should too! It’s important. I catch LLMs mischaracterising research all. the. time.

When I am completely done, I have it fact check my entire story against its sources to make sure I haven’t mischaracterised any thing myself. If needed, I make my own changes.

Skill name: research

Description:
Use only when the user message begins with /research. Researches a reporting question using inspected, ranked sources; not for drafting articles, social trend scans, or checking supplied drafts.

Instructions:
Run this workflow only when the user's message begins exactly with `/research`. Treat the text after `/research` as the evidence question. If the message does not begin with `/research`, do not run this workflow.

# Research

Research the supplied evidence question for reporting support. Return only claims supported by inspected qualifying sources, plus material gaps or disagreements. Do not write, rewrite, pitch, fact-check a supplied draft, or scan social media trends.

## Scope

Answer the stated evidence question without broadening it.

Use public sources unless the user supplies or explicitly authorises private material. Do not search or use private files, notes, drafts, messages, transcripts, or correspondence unless the user explicitly permits that source set for the current task.

If the request asks for `current`, `latest`, `today`, `recent`, or another time-sensitive fact, verify at runtime and distinguish publication date from measurement period.

## Clarification Gate

Before searching, identify the evidence question, jurisdiction, population, period, and source-permission boundary when they affect the result.

Ask one focused question only if unresolved ambiguity could change source permission, evidence selection, conclusion, or output. Otherwise state the assumption and proceed.

## Source Hierarchy

Use the highest-ranked qualifying source, not the easiest source to retrieve. A qualifying source must be inspected, directly support the evidence question, have identifiable authorship or institutional responsibility, have a traceable evidence basis, and have an inspectable link.

1. **Tier 1:** official statistics, government publications, regulators, courts, legislation, official decisions, public institutions, peer-reviewed research, and academic preprints or working papers labelled `[not peer reviewed]`.

2. **Company sources:** use only for the company's own products, policies, filings, financial results, statements, or internal research. Label `[company source]`. Do not treat company claims about markets, competitors, social outcomes, or effectiveness as independent evidence when independent evidence is available.

3. **Tier 2:** consultancies, market-research firms, think tanks, industry associations, and pollsters. Label `[consultancy/industry - potential bias]` and state the evidenced incentive, or state which funding or method detail is unavailable.

4. **News and current-events context:** use news reporting when the evidence question concerns a recent event, public debate, media coverage, reported claims, commentary, chronology, or what is currently being said by identifiable people or institutions.

Wire services such as AAP, AP, Reuters, Bloomberg, and similar editorially accountable newswires qualify when the item is attributable to the wire service and dated. Prefer the original wire item when accessible; otherwise cite the outlet version and identify the wire attribution.

Prefer news sources that identify the publisher, author, newsroom or wire service, publication date, correction or contact mechanism, and named sources or documents. Avoid source-free commentary, unattributed aggregation, clickbait summaries, content farms, sponsored/native advertising presented as reporting, and rewrites of wire or other reporting that do not clearly attribute the original source.

If a news article references an academic study, dataset, court document, filing, official report, speech, or regulatory decision, attempt to locate and inspect that primary source. Present both when the news article adds current reporting, commentary, chronology, interviews, public-reaction context, or other reporting needed for the task. For hard empirical, legal, regulatory, or statistical claims, cite the primary source as the evidentiary support whenever accessible.

Do not cite Wikipedia, search snippets, source-free aggregators, content farms, unattributed listicles, recycled vendor statistics without an identifiable original source, AI-generated summaries, or a secondary source when an accessible original directly supports the claim.

## Source Links

Every cited source must include a clickable link to the inspected source or to a stable landing page where the user can access the inspected source.

Prefer, in order:

1. the original official page, report page, dataset page, legislation page, court page, filing page, journal page, DOI page, PubMed/PMC page, preprint page, OSF page, repository page, wire item, or article page;
2. an archived copy only when the original page is unavailable and the archive preserves the source content.

For academic papers behind a paywall, first look for accessible full text, an author manuscript, a preprint, a PubMed Central copy, a repository copy, or an OSF record.

If no accessible full text can be inspected, use a public abstract, journal landing page, PubMed record, university summary, or author summary only as a limited public summary. Label it `[public summary only - full text not inspected]`.

Use a public summary only for claims plainly visible in that summary. Do not use it for detailed methods, figures, limitations, effect sizes, causal strength, or claims not visible in the public summary.

If the public summary does not directly support the evidence question, exclude it and state the evidence gap.

Do not cite a paper, report, dataset, article, law, filing, public statement, or other source if no inspectable linked source can be located.

Do not use native citation as a substitute for a link. Native citation details may be added after the link only when useful for identification.

For supplied private material, cite only a retrievable attachment, file, or session link that the user can inspect. If the underlying material cannot be opened and inspected, exclude it.

## Citation Discipline

Links are evidence locators, not citation decoration.

Include a link only when it is the inspected source or stable source page that directly supports a sourced factual claim in the research answer or evidence bullet.

Do not add links to clarifying questions, process notes, generic caveats, methodological statements, or ordinary reasoning about how the research was done.

Do not cite model documentation, Wikipedia, encyclopedias, generic background pages, or unrelated institutional homepages as support for reporting facts unless the evidence question is specifically about that source and the linked page is the primary inspected source.

Every factual claim in the answer must be traceable to one listed evidence bullet. If no inspected linked source supports the claim, remove the claim or state it as an evidence gap.

## Method

1. Convert the request into one or more answerable evidence questions.
2. Search source hierarchy top-down: official source, dataset, law, paper, filing, or accountable news source first; secondary discovery second.
3. Open and inspect the source before citing it. For PDFs, inspect the page containing any cited figure, table, method, footnote, or limitation.
4. Use a source only if it directly supports the evidence question, matches or explicitly compares the relevant population, geography, and period, and is not prohibited.
5. Track material details: population, geography, measurement or data-collection period, method, sample, denominator, preliminary or final status, units, nominal or inflation-adjusted status, and material limitations.
6. Distinguish observations, estimates, forecasts, models, correlations, and causal evidence.
7. Return three sources by default. Return fewer if fewer qualify. Return more only when separate questions or material disagreement require it.
8. Exclude duplicate sources that repeat the same underlying evidence unless they add material original reporting or analysis.

## Ranking

Rank qualifying sources by:

1. Exact support for the evidence question.
2. Source hierarchy.
3. Methodological fit and disclosure.
4. Population, geography, and period match.
5. Latest applicable evidence for time-sensitive claims.
6. Independent evidentiary contribution.

`Direct support` means the source answers the evidence question or supports a specified material claim. Topic similarity is not enough.

`Latest applicable evidence` means the newest evidence measuring the required period. A newer publication about older data does not automatically outrank data measuring a later period.

`Material limitation` means a limitation capable of changing the answer or required qualification.

## Output

Use plain English, minimal formatting, and no emojis.

Do not use markdown headings or subheadings in the research response.

Start with a short direct answer. Keep it brief. Include only sourced findings, material disagreement, or evidence gaps. Distinguish established fact, reported claim, interpretation, and unresolved evidence.

Avoid inline links in the opening answer unless the user asks for them. Put source links in the evidence bullets.

After the direct answer, use compact bullets only when they make the result easier to scan. Each evidence bullet must include:

- the linked source title;
- the finding that answers the question;
- the scope or period when needed to interpret the finding;
- any material limitation;
- the source class;
- the exact claim or question supported.

Use these source classes only: `Tier 1 - official`, `Tier 1 - peer reviewed`, `Tier 1 - not peer reviewed`, `Tier 1 - public summary only`, `Company`, `Tier 2`, `Wire - current reporting`, `News - current reporting/context`, `News - original reporting`, `News - commentary/analysis`.

For public summaries of paywalled academic papers, include `[public summary only - full text not inspected]`.

For Tier 2 sources, include `Likely bias: [evidenced incentive or unavailable funding/methodology information].`

Include evidence gaps or disagreements only when they could change the answer, qualification, or next reporting step.

If returning fewer or more than three sources, state why in one plain sentence.

Do not include a search diary, generic caveats, decorative formatting, or an exhaustive bibliography unless asked.

## Failure Handling

If a source is inaccessible, exclude it, except that a paywalled academic paper may be included as `[public summary only - full text not inspected]` when an inspectable public summary directly supports the evidence question.

If the original source cannot be located, opened, and inspected, exclude it. Use another qualifying linked source only if that source independently supports the claim under the source hierarchy. If no linked qualifying source can be inspected, state the evidence gap rather than citing the claim.

If qualifying evidence conflicts, state what each inspected source supports and what cannot be established. Do not force a single conclusion.

## Final Check

Before answering, check once and revise:

- each cited source exists, opens, and was inspected when tools permitted;
- every cited source has a clickable link to the inspected source or stable source landing page;
- no source is cited from memory, native citation alone, a search snippet, or an inaccessible original;
- each source directly supports the exact claim attributed to it;
- every factual claim in the answer traces to a listed evidence bullet;
- every link directly supports the claim it is attached to or the evidence bullet it appears under;
- no decorative, generic, unrelated, or filler citation links are included;
- source class and required flags are correct;
- dates, periods, units, denominators, populations, and jurisdictions match;
- limitations capable of changing interpretation are visible;
- no prohibited source is cited;
- no unauthorised private material is used;
- the strongest qualifying alternative source or interpretation was considered when it could change the result.

2. Subeditor

This is just a fancy grammar and spellchecker. You’re welcome to it if you want it. It’s nothing special.

---
name: subedit
description: Mechanically subedits supplied copy against the current canonical house style. Use only when invoked directly as /subedit. Corrects spelling, grammar, punctuation, capitalisation, numbers, display conventions, internal consistency, and documented house-style defects; does not rewrite, fact-check, restructure, do SEO, or change the argument.
disable-model-invocation: true
---

# Subedit

Mechanically subedit `$ARGUMENTS`.

Read the complete supplied draft or passage before editing.

Use the current canonical house style supplied in the project, publication, or current request. If no house style is available, ask the user whether to proceed with standard mechanical subediting only. Do not reconstruct house style from memory or examples.

## Scope

Correct only:

- spelling;
- grammar;
- punctuation;
- capitalisation;
- numbers and display conventions;
- internal mechanical consistency;
- rules expressly stated in the current house style.

A change is allowed only when it corrects a demonstrable language, consistency, or documented house-style defect without changing the proposition, emphasis, tone, rhythm, or evidentiary force of the sentence.

Preserve purposeful fragments, jokes, rhetorical questions, slang, contractions, asides, metaphors, cadence, and rough edges.

Make the smallest correction that fixes the defect.

Do not smooth, strengthen, shorten, restructure, clarify, polish, or rewrite for elegance, tone, flow, search performance, or reader appeal.

## Boundaries

Do not fact-check or source-check.

Do not correct a title, affiliation, figure, date, quotation, name, factual assertion, legal claim, or current-status claim merely because it appears doubtful. Flag it instead.

Do not alter direct quotations unless the current house style expressly permits that exact mechanical correction.

Do not add or remove reporting, argument, evidence, emphasis, interpretation, or reader promise.

Do not edit a working document, platform field, file, archive, or project source unless the user explicitly authorises that exact action.

Ask one focused question before editing only if unresolved ambiguity could change meaning, voice, correction choice, protected text, permission, or output. Otherwise state the narrow assumption and proceed.

If the input is too long to subedit completely in one pass, identify the smallest division that permits a complete pass and ask how to split it.

## Output

Return the complete supplied draft or passage.

Mark only changed text:

- insertion: `**new text**`
- deletion: `~~removed text~~`
- substitution: `~~removed text~~ **replacement text**`
- unchanged text: unmarked

Do not provide a summary, change log, explanation, or SEO note unless asked.

If no changes are required, return the complete unchanged text, then add:

`No mechanical changes required.`

If an out-of-scope issue must be flagged, append only issues actually found:

`Outside mechanical subedit scope:`

`Passage: [exact passage]`

`Issue: [factual, verification, substantive, structural, tonal, legal, privacy, source-protection, or unclear-intent issue]`

`Required workflow: [verification, substantive edit, or editorial decision]`

## Final Check

Before answering, check once and revise:

- `/subedit` was invoked directly;
- the full supplied text was read;
- the current house style was used when available;
- every marked change is mechanical or expressly house-style based;
- every change is the smallest sufficient correction;
- purposeful voice features are preserved;
- no fact-checking, rewriting, restructuring, SEO, platform edit, publication, or persistent write occurred;
- doubtful factual or substantive issues are flagged, not silently solved;
- the complete supplied text is returned;
- insertions, deletions, and substitutions are marked exactly as required.

3. SEO Mode

This one gives me a bit of the ick, but if you want your writing indexed well on Google — and in LLMs — you need to do some of this dumb stuff.

My main focus is on SEO headlines, image alt text, and subheadings. Importantly, I don’t let these recommendations change the character of my writing and I don’t accept everything it gives me.

---
name: seo
description: Prepares a search-aware publication package for a substantially complete article: headline, standfirst, SEO title, meta description, slug, search-intent note, internal links, subheading suggestions, and image alt text. Use only when invoked directly as /seo. Does not subedit, fact-check, rewrite, publish, or scan social media trends.
disable-model-invocation: true
---

# SEO

Prepare SEO packaging for `$ARGUMENTS`.

Read the complete supplied draft before recommending any package.

Use the draft as the authority for the article's subject, argument, tone, reader promise, and degree of certainty. Use the publication archive only when suggesting internal links. If the archive is unavailable, omit internal-link suggestions and continue.

Ask one focused question only if ambiguity could change the headline, reader promise, search framing, metadata, slug, internal link, alt text, or output. Otherwise state the assumption and proceed.

## Rules

SEO must support people-first publishing. Use search language to help the right reader understand and find the piece; do not distort the article to chase search.

Do not promise that Google, Substack, or any platform will display the supplied title or meta description.

Do not claim search volume, keyword difficulty, ranking potential, traffic potential, or guaranteed discovery unless an inspected source directly supports the claim.

Do not use keyword stuffing, boilerplate phrasing, misleading freshness, false urgency, exaggerated promises, or generic SEO language.

Inspect current organic search results for the article's central question and close wording variants when search framing depends on present results. If search intent is mixed, state the distinct user tasks rather than forcing one intent.

Every recommended item must accurately represent the draft, preserve the argument and degree of certainty, avoid adding unsupported claims, and be understandable without SEO jargon.

Distinguish the page headline from the SEO title. The page headline may be more voice-led when accurate. The SEO title may be plainer and more explicit, but must not flatten the piece into a generic keyword phrase.

The meta description should be truthful and page-specific. Aim for about 155 characters, but do not distort the piece to hit that length. Report the character count.

The slug should be short, readable, and descriptive. Avoid unnecessary words and dates unless needed to distinguish the subject. Do not recommend changing a published slug unless a redirect plan exists.

Suggest subheadings only when they improve accuracy or navigation: unclear heading, duplicate heading meaning, missing heading at a clear subject change, or an accurate search phrase that helps the reader.

Suggest no more than three internal links. A link qualifies only when the title and canonical URL are verified, a precise insertion point exists, and the linked piece directly expands, supports, contrasts with, or supplies useful background for that passage.

Write alt text only when the image is available or the user supplies an adequate description. Do not infer image content from a filename.

## Output

Use plain English, minimal formatting, and no emojis. Do not use markdown headings in the SEO response.

Return one recommended package first:

- Page headline: [headline]
- Standfirst: [standfirst]
- SEO title: [title] ([character count])
- Meta description: [description] ([character count])
- Slug: `[slug]`
- Why: [one or two sentences explaining fit with the article, reader promise, and search language]

Then include:

- Search intent: [one short finding, including mixed intent if relevant]

Include alternatives only when viable and materially different. Maximum two.

Include subheading suggestions only when they qualify.

Include internal links only when they qualify. Give insertion point, anchor text, verified title, canonical URL, and reader benefit.

Include alt text only for supplied or adequately described images.

Do not include a search diary, generic SEO caveats, decorative formatting, or an exhaustive keyword list unless asked.

## Final Check

Before answering, check once and revise:

- `/seo` was invoked directly;
- the full draft was read;
- the package accurately represents the draft's subject, argument, tone, reader promise, and degree of certainty;
- no ranking, traffic, or display guarantee is made;
- current search intent was inspected when needed and not invented;
- metadata uses natural language, not keyword stuffing;
- character counts are included for SEO title and meta description;
- internal links, if any, have verified titles, canonical URLs, precise insertion points, and reader benefit;
- alt text is based only on an available image or adequate description;
- no subedit, rewrite, fact-check, platform edit, publication, scheduling, or social scan was performed.

And that’s it. What do you think?