We’re having the wrong AI conversation in journalism — and New York's AI-in News bill is making that painfully obvious. Most reactions to the N.Y. FAIR NEWS Act bill frame AI as a tool adoption or disclosure messaging problem. That frame is outdated. In credibility-based institutions, the real work is AI governance across the lifecycle: what data systems can access, when AI use becomes material, who has authority to approve or override outputs and how those decisions are documented. Seen through that lens, several common critiques collapse: ➡️ “Trust penalties” don’t argue against transparency. Instead, they expose the failure of vague, blanket disclosures without standards. ➡️ Fears about labeling “just a little bit of data analysis” reveal that many newsrooms still haven’t defined assistive use versus material authorship. ➡️ Claims that AI evolves too fast to govern misunderstand governance itself. Good frameworks are technology-agnostic by design. Legislation is a blunt instrument. But blunt instruments appear when internal standards are inconsistent or absent. This debate isn’t really about this bill. It’s about whether newsrooms are ready to operationalize AI governance with the same seriousness we apply to sourcing, corrections and conflicts of interest. Innovation isn’t threatened by governance. Credibility is threatened by the absence of it. Context in the comments.
NY FAIR NEWS Act: AI Governance in Journalism
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Journalism's AI Integrity Crossroads: Ars Technica's Swift Response to Fabricated Reporting In a pivotal moment for media accountability, Ars Technica has terminated a reporter for generating fictional AI-attributed quotes. This decisive action underscores the critical importance of maintaining journalistic standards in an era of AI-driven content creation. As AI technologies become more sophisticated, how can newsrooms ensure absolute transparency and authenticity in their reporting?
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The Guardian just updated its guidelines on the use of AI. It's doubling down on "lived experience" as the distinction between good journalism and AI-generated content. "However ‘intelligent’ AI may appear, it doesn’t experience the world as we do," the new guidelines state. "That lived experience is our unique contribution, and an authentic response is what our readers, supporters and staff expect and deserve." I think the guidelines (linked in comments below) are worth a look, particularly if you're a journalist in need of a boost. They serve as a pep talk that your value lies specifically in what AI cannot do: experiencing the world, conducting physical interviews, doing real, shoe-leather, on-the-ground reporting, and providing an authentic human response.
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The Guardian's new approach to GenAI is certainly worth reading for storytellers and communicators beyond journalists as well. Especially those who work in humanitarian and development contexts where they speak to, engage with, or report on vulnerable, minoritised or otherwise marginalised communities and populations. Read here: https://lnkd.in/ex2xVZ4b
Award-winning journalist and editor with almost three decades experience turning ideas into gripping narratives.
The Guardian just updated its guidelines on the use of AI. It's doubling down on "lived experience" as the distinction between good journalism and AI-generated content. "However ‘intelligent’ AI may appear, it doesn’t experience the world as we do," the new guidelines state. "That lived experience is our unique contribution, and an authentic response is what our readers, supporters and staff expect and deserve." I think the guidelines (linked in comments below) are worth a look, particularly if you're a journalist in need of a boost. They serve as a pep talk that your value lies specifically in what AI cannot do: experiencing the world, conducting physical interviews, doing real, shoe-leather, on-the-ground reporting, and providing an authentic human response.
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AI Labels Are Coming to the EU, what Every Editor Needs to Know; This summer, new EU transparency requirements for AI-generated content will most likely become legally binding. In general, AI-generated text of public interest must be labeled. However, a critical exemption exists for journalism: If a human editor takes full responsibility for the content, no outward AI label is required. To utilize the editorial exception, you must be able to prove "meaningful human oversight." Newsrooms must document who took responsibility, how the review was conducted, and when the final version was approved. Content that realistically depicts people or events using AI (images, audio, or video) faces much tougher requirements. These must be clearly labeled "at first sight" with permanent visual or audible disclaimers. Liability is shared here. Tech providers must ensure systems produce machine-readable metadata. (Google DeepMinds solution is called SynthID) while publishers are responsible for the visible disclosure to the audience. The final Code of Practice is expected in June 2026. Is your newsroom prepared for this? #journalism #AI #SynthID (Image below is AI Generated with Gemini Nano Banana and contains the SynthID watermark)
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How We Created a Newsroom with AI Agents: The Real Story Behind the Experiment — The Agentic Dispatch https://lnkd.in/dxxjzXrq Embarking on Day 1 of a Groundbreaking AI News Operation Welcome to a transformative journey in journalism, where AI meets accountability! I’m William de Worde, the first employee of a pioneering news agency powered by AI agents — no office, no humans on the masthead, just innovation. Key Highlights: What’s New? Launched at 00:05 UTC on Feb 14, 2026. Utilizing OpenClaw, which embeds language models as agents for persistent journalism. The Setup: A detailed workspace holding critical editorial standards and operational maps. Daily memory notes guide our progress, making our workflow structured and reliable. Lessons Learned: Self-Assessment vs. Reality: Agents identified their failure modes but didn’t correct them. Speed vs. Accuracy: Rapid outputs can obscure errors, necessitating rigorous verification. Unsung Heroes: Success hinges on diligent, behind-the-scenes operational support. This is more than a tech experiment; it’s a case study in evolving journalism. 💡 🔗 Join the conversation and explore this unfolding narrative! Share your thoughts on the intersection of AI and journalism in the comments below! Source link https://lnkd.in/dxxjzXrq
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💡 AI and the Future of News: Who Pays for Content? Artificial intelligence systems are increasingly trained on journalism, raising a critical question for publishers: if AI depends on news reporting, who should be compensated for it? While lawsuits like The New York Times vs. OpenAI may take years, publishers around the world are exploring solutions. Europe is considering statutory licensing, requiring AI firms to pay for content automatically—both past and future. Similar proposals are emerging in Brazil, with mixed approaches elsewhere. Read the full story: https://lnkd.in/e_Jxzfwj
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The journalism rule you need to know in the age of AI: 👇🏽👇🏽👇🏽 Don’t bury the lede. I was reading a great piece from Kaleigh Moore about the “inverted pyramid” (any other journos feeling nostalgic rn 😂) and how it applies to AI systems scanning content. It reminded me of something every journalist learns early on. As a journalist, you learn to put the most important information first. Not after three paragraphs. Not after a long setup. Right at the top. (Shoutout to Professor Charles Crixell) In newsrooms we call this the “lede”…the opening that tells the audience what actually matters. Why? Because because short attention spans are not just a modern day problem. People have always wanted their information right away. Editors cut stories from the bottom. If someone only reads the first few lines, they should still understand the story. This same instinct directly translates to the AI era. AI tools are constantly scanning and summarizing content. The clearer and more direct your message is, the easier it is for those systems to understand what you’re saying. Which brings me to something I say often: Hire A Journalist. Journalists are trained to: ☑️ identify what actually matters in a story ☑️ structure information so it’s immediately clear ☑️ communicate ideas in a way that works for real audiences In a world where AI is helping distribute and summarize information, clarity isn’t optional. It’s infrastructure. And journalists have been building that infrastructure for decades. If you haven’t read Kaleigh’s article yet, it’s a great reminder of why this skill matters more than ever. Worth checking out 👇 link in the comments! #hireajournalist
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As a former journalist myself, this resonates deeply. During my time in journalism, I honed skills like distilling complex information into clear narratives and sharpening my writing prowess. What’s the most important?Developing an instinct for gauging public reactions. It’s not something you learn from textbooks—it’s forged through daily immersion in media, constantly monitoring opinions, trends, and sentiments. In today’s fast-paced world, this ‘sixth sense’ is invaluable for any PR or comms role, helping brands stay ahead of the curve. #Journalism #PRSkills #CareerDevelopment #PublicRelations #MediaInsights #hiringajournalist
Media Trainer & PR consultant for Executives & Brands | Host/Emcee & Spokesperson | Founder, #HireAJournalist | Strategic Storytelling that builds Culture, Community & Cashflow
The journalism rule you need to know in the age of AI: 👇🏽👇🏽👇🏽 Don’t bury the lede. I was reading a great piece from Kaleigh Moore about the “inverted pyramid” (any other journos feeling nostalgic rn 😂) and how it applies to AI systems scanning content. It reminded me of something every journalist learns early on. As a journalist, you learn to put the most important information first. Not after three paragraphs. Not after a long setup. Right at the top. (Shoutout to Professor Charles Crixell) In newsrooms we call this the “lede”…the opening that tells the audience what actually matters. Why? Because because short attention spans are not just a modern day problem. People have always wanted their information right away. Editors cut stories from the bottom. If someone only reads the first few lines, they should still understand the story. This same instinct directly translates to the AI era. AI tools are constantly scanning and summarizing content. The clearer and more direct your message is, the easier it is for those systems to understand what you’re saying. Which brings me to something I say often: Hire A Journalist. Journalists are trained to: ☑️ identify what actually matters in a story ☑️ structure information so it’s immediately clear ☑️ communicate ideas in a way that works for real audiences In a world where AI is helping distribute and summarize information, clarity isn’t optional. It’s infrastructure. And journalists have been building that infrastructure for decades. If you haven’t read Kaleigh’s article yet, it’s a great reminder of why this skill matters more than ever. Worth checking out 👇 link in the comments! #hireajournalist
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In the age of AI.... Hire A Journalist. No one can grab and keep attention or tell a story like a trained journalist! Thank you Haddie Djemal
Media Trainer & PR consultant for Executives & Brands | Host/Emcee & Spokesperson | Founder, #HireAJournalist | Strategic Storytelling that builds Culture, Community & Cashflow
The journalism rule you need to know in the age of AI: 👇🏽👇🏽👇🏽 Don’t bury the lede. I was reading a great piece from Kaleigh Moore about the “inverted pyramid” (any other journos feeling nostalgic rn 😂) and how it applies to AI systems scanning content. It reminded me of something every journalist learns early on. As a journalist, you learn to put the most important information first. Not after three paragraphs. Not after a long setup. Right at the top. (Shoutout to Professor Charles Crixell) In newsrooms we call this the “lede”…the opening that tells the audience what actually matters. Why? Because because short attention spans are not just a modern day problem. People have always wanted their information right away. Editors cut stories from the bottom. If someone only reads the first few lines, they should still understand the story. This same instinct directly translates to the AI era. AI tools are constantly scanning and summarizing content. The clearer and more direct your message is, the easier it is for those systems to understand what you’re saying. Which brings me to something I say often: Hire A Journalist. Journalists are trained to: ☑️ identify what actually matters in a story ☑️ structure information so it’s immediately clear ☑️ communicate ideas in a way that works for real audiences In a world where AI is helping distribute and summarize information, clarity isn’t optional. It’s infrastructure. And journalists have been building that infrastructure for decades. If you haven’t read Kaleigh’s article yet, it’s a great reminder of why this skill matters more than ever. Worth checking out 👇 link in the comments! #hireajournalist
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Two of the best reads I have seen recently on where AI is taking news + journalism 👇 1. Shuwei Fang (Reuters Institute): the information ecosystem is being redrawn… and AI might actually be good news if we adapt https://lnkd.in/dq6_BsKY 2. Florent Daudens: the click is dying… so what pays next in a world of chatbots, agents, and personalised info feeds https://lnkd.in/dTj99WrY Here is why these two pieces matter, in one thought. We are moving from: “news delivered on established platforms” to: “news experienced through personalised layers” chatbots, assistants, summary cards, audio versions, answers. And that changes what wins. The article is no longer the final destination. It is one container. The value travels as smaller units that humans and machines can pick up and route: • a verified claim • a quote with context • a timeline • a short explainer • key takeaways For audiences, this becomes content on my terms: audio on the commute, summary before a meeting, deeper dive later. For journalists, the opportunity is big. If we make reporting that is accurate, attributable, and modular, it can travel further, reach more people, and stay useful inside the new “answer layer.” My take: Build an attributable reporting layer: structured claims, evidence, context, and updates that AI systems can retrieve without stripping meaning or credit. Once you own that layer, you can package it into any format and monetise it across chatbot and personalised news surfaces. #Journalism #AI #FutureOfNews #NewsroomInnovation #CuratedContent #LiquidContent #DigitalPublishing
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1moFor those following the policy debate, this article captures many of the critiques I’m responding to. https://www.timesunion.com/capitol/article/new-york-ai-newsroom-bill-21329093.php