“From News to Newscast — at Lightning Speed.”

Our A.I. Policy / Checklist shown at the bottom of the page is based on a solid (and growing) amount of credible research on A.I. in journalism and TV news. Below you will find a reading list with quick takeaways. This focuses on research from accredited universities / think tank work and from several high quality industry reports.

Core academic & research reports (recent)

• Tow Center @ Columbia J-School — “AI in the News: Retooling, Rationalizing, and Reshaping Journalism” (2024)  One of the most-cited deep dives. 134 interviews across 35 news orgs (Guardian, WaPo, FT, etc.). Maps real newsroom use cases (editing, search, tagging, personalisation), governance gaps, and labor impacts.

• Reuters Institute (Oxford) — Digital News Report (2024)  AI module Large, comparative audience data on comfort/trust with AI in news; people are most wary of AI on sensitive beats (politics), more accepting for back end tasks.
  
  
  
• Nikki Usher (Univ. of San Diego) — “Generative AI and Journalism: Hype, the Always-Already New…” (2025)  Scholarly essay cutting through hype; frames gen-AI against long tech cycles in news, highlights black-box risks and accountability. (Open access)  

• Tow Center — “Journalism Zero: How Platforms and Publishers are Navigating AI” (2025)  Strategic look at governance, platform relationships and policy—all directly relevant if you’re building tooling for newsrooms.
  

• European Broadcasting Union — “News Report 2024: Trusted Journalism in the Age of Generative AI”  Based on interviews with 40 media leaders/experts; focuses on broadcast/public-service newsrooms (ethics, workflows, verification).  

• Journalist’s Resource (Harvard Shorenstein) — AP & BBC embedded AI research (2024)  Summarizes two embedded studies inside AP/BBC trial;  practical lessons on change management, expectations, and newsroom buy-in.  

•  Virginia Tech (Master’s thesis, 2025) — “Artificial  Intelligence in 2024: A Thematic Analysis of Media Coverage“  Analyzes NYT/WSJ/WaPo’s AI framing; useful for understanding how mainstream coverage shapes audience expectations of AI in news.
  

• CSUSB (2025) — “Is AI Bias in Journalism Inherently Bad?”  Scholarly take on algorithmic bias, representation, and news production—handy for ethics/policy sections.  (Open access)  

Broadcast-specific industry context

• TV Tech / TVBEurope — AI in broadcast production  Up-to-date overviews of how broadcasters deploy AI for highlight clipping, captioning, translation, lip-sync localization, and playout; good for workflow examples.  

• Reuters — “Reuters and AI” policy page
  How a major wire discloses AI usage and handles attribution—useful model policy language.  

• AP News — global media groups’ “News Integrity in the Age of AI” principles (2025)  Cross-association statement (EBU, WAN-IFRA, etc.) on licensing, attribution, and misinformation—good for governance decks.  

Why this matters for TV newsrooms

• Audience trust: People are ok with AI behind the scenes (transcripts, search, highlights) but uneasy when it touches political content or is used “unseen.” Clear disclosure policies and human-in-the-loop editing are key.  

• Workflow wins: Fast wins are speech-to-text, automated metadata, clip-finding, subtitling, and translation/localization for OTT and social. Sports and weather lead adoption.  

• Governance: Write-down rules for provenance, fact-checking, disclosure, and red-team testing. Borrow language from Reuters/EBU pieces above.