What if you could use AI as the institutional memory for your newsroom?
By now you’re probably sick of hearing about AI and ChatGPT. I know I am, and I literally wrote a book (okay fine, it’s just an ebook) about this stuff.
But if you’ll humor me for a minute, there’s a deeper, more subtle conversation to be had about all this that a lot of people don’t seem to really understand at the moment.
These kinds of tools aren’t just fancy new toys that can help us do stuff quicker — although they certainly do that, too — they’re also a potential game-changer when it comes to preserving institutional memory. If used properly, I believe they have the potential to revolutionize the way we understand our communities, our body of work, and how we preserve our collective knowledge.
Picture this: An AI model trained on your newsroom’s archives and its entire body of work, along with any of the other community- or org-specific reports, information, documentation, and data you can find and upload. By analyzing this vast trove of data, an LLM could identify patterns and connections that might not be immediately apparent to human analysts. It could find gaps, opportunities, and challenges that you might never have noticed without the assistance of these tools.
Then you — and members of your community — can have conversations with that data and get (mostly) useful answers.
This, I think, is the kind of big-picture approach to AI that has the potential to make an actual difference in the way we understand and relate to not just our own work, but the role and relationship of that work within our communities. It goes beyond even the impressive ability to glean institutional strategy and advice, self-reflection, and archival value from these tools. And it might also serve as a pretty attractive subscriber or public benefit.
It would essentially be like having a local news oracle that can tell you everything you need to know about your community and your role within it.
But wait, there’s more!
Custom-trained LLMs and AI assistants could also help us fill in missing details and piece together historical narratives that might have otherwise remained incomplete. By having a more complete and nuanced understanding of our communities, we can create better-informed news stories that truly reflect the needs and interests of the people we serve.
As always (and as discussed ad nauseum in the ebook), we have to be mindful of the limitations and potential biases of the AI systems we create and use. Careful consideration and maintenance of data sources and algorithmic biases is crucial to ensuring that these tools are accurate and trustworthy. And while AI can help us identify connections and patterns, human interpretation and analysis are still crucial for creating a comprehensive understanding of complex historical events and themes.
Still, the power of AI to revolutionize local news and preserve institutional memory cannot be overstated. By unleashing the full potential of this technology, we can create a more informed and engaged society, one that truly reflects the needs and interests of the people we serve.
A version of this post first appeared in my ebook, “Beginner’s prompt handbook: ChatGPT for local news publishers.”
Joe Amditis is associate director of products and events at the Center for Cooperative Media. Contact him at amditisj@montclair.edu or on Twitter at @jsamditis.
About the Center for Cooperative Media: The Center is a grant-funded program of the School of Communication and Media at Montclair State University. Its mission is to grow and strengthen local journalism, and in doing so serve New Jersey residents. The Center is supported with funding from Montclair State University, John S. and James L. Knight Foundation, the Geraldine R. Dodge Foundation, Democracy Fund, the New Jersey Local News Lab (a partnership of the Geraldine R. Dodge Foundation, Democracy Fund, and Community Foundation of New Jersey), and the Abrams Foundation. For more information, visit centerforcooperativemedia.org.
