How AI assists our newsroom
AI is Mid uses a tightly scoped tool stack to accelerate research, surface story ideas, and streamline production workflows. Editors rely on retrieval augmented search to sift through academic papers and policy filings, and we use transcription models to convert interviews into reviewable transcripts. Generative models produce early outlines that our human reporters expand, revise, and fact-check. Visual editors leverage diffusion models for concept art before rebuilding final images pixel-by-pixel in traditional design software.
Every deployment is documented in an internal tooling register that tracks the model vendor, version number, purpose, and any prompt templates. Reporters must log the prompts and seed references that informed a piece, giving our fact-checkers a traceable record of machine involvement. No raw AI output is published without human review, revision, and explicit approval from the section editor.
Editorial guardrails and human review
We follow a “human-first” review loop. Drafts that were shaped with AI assistance undergo two editorial passes: the first confirms accuracy, nuance, and sourcing, and the second checks for synthetic phrasing, hallucinations, and disclosure completeness. Senior editors reserve the right to strip AI-generated passages, request additional reporting, or reject the content outright if it fails our ethical standards.
Podcast production follows a similar process. We use AI for noise reduction, transcript polishing, and segment markers, but hosts and producers make all editorial decisions about narrative structure and final cuts. Each episode receives a disclosure card that outlines which segments were machine assisted, which were fully human, and how listeners can contact us if something seems amiss.
Image generation and attribution
Illustrations tagged as AI-assisted begin as generative drafts that help our designers explore composition, color palettes, and metaphors. Designers rebuild final assets in Photoshop, Figma, or Blender, layering original typography and texture work on top of the AI foundation. We watermark behind the scenes with invisible metadata that lists the creative tools, artist, and revision history.
When we publish, captions identify the human art director and note whether automation contributed to ideation or rendering. Any dataset or prompt references used to train custom models are listed in a footnote so artists can evaluate whether their own work may have been included. If you believe we have misattributed or overstepped usage, reach out to art@aiismid.com and we will investigate.
Data privacy and prompt hygiene
We never feed personally identifiable information, unpublished reporting, or confidential documents into third-party AI services. Sensitive material lives in encrypted storage that is isolated from experimentation environments. When we do use generative APIs, we opt out of data retention, disable training, and renew vendor audits quarterly.
Our newsroom prompt library excludes reader emails, whistleblower tips, and embargoed press materials. If a workflow requires anonymized summaries, reporters manually strip identifying details before engaging the tooling. The production staff receives regular training on prompt hygiene and must pass quarterly assessments that cover privacy law, platform policies, and real-world case studies.
Reader disclosures and transparency labels
Every article includes an AI disclosure badge near the byline. The badge outlines which workflow stages used automation—research, drafting, editing, image generation, audio mastering—and the specific tools involved. Hover states and footnotes link to this page for the full policy, and changelog updates explain when new tools enter or leave the stack.
Our CMS enforces the badge by preventing publication until reporters choose a disclosure template. Editors can append custom notes describing unusual AI involvement, such as simulated interviews or synthetic data tests. These notes are indexed by search engines so that platforms like Google’s Perspectives results understand the provenance of our coverage.
Feedback, audits, and policy evolution
We invite readers, researchers, and other publishers to audit our disclosures. If you spot a missing label, unclear attribution, or potential misuse of AI outputs, email disclosures@aiismid.com with the URL, timestamp, and details. We respond within three business days and document any corrections in a public changelog.
This policy was last refreshed on October 30, 2025. We review it quarterly alongside the Content License page to ensure our guardrails evolve with new regulation and community expectations. Significant revisions trigger on-site banners, newsletter updates, and postscript mentions in the AI is Mid Digest. Subscribe or follow us on Threads to stay informed when the disclosure framework evolves.