“From News to Newscast — at Lightning Speed.”

Our AI Policy / Checklist shown at the bottom of the page is based on a solid (and growing) amount of credible research on AI 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.

1. Tool Inventory & Audit

• Maintain a live list of all AI / automation tools in use (internal, third-party, plugins)
• For each: note purpose, data inputs/outputs, responsible owner

2. Use Cases & Permissions

• Allowed uses (e.g. transcription, metadata tagging, drafting suggestions)
• Restricted uses (e.g. unsupervised content generation in news articles, deepfake video)
• Human in the loop: every piece of AI output that goes public should pass an editor’s review

3. Transparency & Disclosure

• Disclose usage publicly (website, footers, byline tags) when AI is involved
• Be clear on what type of AI (ML assist, generative, summarization)
• Keep internal logs of AI usage for audit

4. Fact-checking & Verification

• Require source citation for any claims or data
• Any AI-generated content must undergo verification by reporter/editor
• Maintain a “challenge / red team” process: regularly test the system for hallucinations or bias

5. Data Privacy & Confidentiality

• Avoid uploading sensitive, unpublished, or source identity data into external AI tools
• Use on-prem or secure APIs when possible
• Define and enforce data retention / deletion policies

6. Bias / Fairness / Inclusion

• Monitor for systemic bias (e.g. coverage gaps, demographic representation)
• Include diverse review panels for AI outputs
• Routinely evaluate whether AI pipelines deepened or alleviated inequality in coverage

7. Training & Literacy

• Require all relevant staff to undergo AI ethics / tool training
• Provide regular updates / refreshers as tools change
• Create a glossary / best practices internal reference

8. Monitoring, Audit & Revision

• Schedule quarterly audits of AI usage, failures, and user complaints
• Document errors / near misses and revise policy accordingly
• Mandate incident escalation protocols (if AI output has serious error, public retraction path)

9. Governance & Accountability

• Assign a AI oversight lead / committee (editor + tech + legal)
• Required approval workflows for deploying new AI tools. 
• Public reporting (annual) of AI usage and any errors / challenges