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AI Filters Data, But People Decide What Matters: Inside the Union Leader's Editorial Experiment

  //science-technology.news-articles.net/content/2 .. ide-the-union-leader-s-editorial-experiment.html
  Published in Science and Technology on Wednesday, November 26th 2025 at 7:32 GMT by New Hampshire Union Leader

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AI Filters Data, But People Decide What Matters: An Inside Look at the Union Leader’s Editorial Approach

The Union Leader’s latest feature, “Andrew Mitchell: AI Filters Data, People Decide What Matters,” opens with a candid exploration of how a small‑town New England newspaper is grappling with the twin promises and perils of artificial intelligence. At the heart of the story is Andrew Mitchell, the paper’s senior editor and a veteran journalist who has been steering the publication’s transition from print to a digitally‑first, data‑driven platform. Mitchell’s perspective offers a microcosm of a broader conversation that is reshaping the media landscape: the balance between algorithmic curation and human editorial judgment.

1. The Genesis of the AI Experiment

Mitchell recalls how, three years ago, the Union Leader launched a pilot project called “Smart Feed”. The idea was straightforward: use machine‑learning models to sift through the vast amounts of content—both internal stories and external news—that the paper’s readers encounter each day. By analyzing past reading habits, social media shares, and engagement metrics, the system would surface stories that were most likely to resonate with local audiences.

What the pilot quickly revealed, however, was that an algorithm alone could not capture the nuanced context that mattered to the paper’s readership. “People still have to decide what’s important,” Mitchell says, underscoring the central thesis of the article: AI is a tool, not a replacement for editorial discernment.

2. From “Filtering” to “Curating”

The article draws a sharp distinction between filtering and curating. Filtering, Mitchell explains, is the mechanical process of sorting content based on data points—click‑through rates, time‑on‑page, or even sentiment scores. Curating, on the other hand, involves a human judgment call about the story’s relevance, ethical implications, and alignment with the paper’s values.

The Union Leader’s editorial board adopted a hybrid workflow: AI flags a shortlist of potential stories, and editors review and refine that list. This model allows the newsroom to keep pace with the speed of digital consumption while preserving a sense of editorial integrity. Mitchell’s narrative is peppered with anecdotes of how this approach has already helped the paper spot emerging local issues—such as a sudden spike in traffic accidents near a new highway interchange—long before a human editor would have noticed.

3. Navigating Bias and Accountability

The article does not shy away from the thorny question of bias. AI models trained on historical data can perpetuate existing prejudices, a concern that has sparked heated debates in journalism circles. Mitchell acknowledges that the Union Leader has taken concrete steps to mitigate this risk:

1. Transparent Algorithms – The paper’s data scientists publish a summary of the models’ logic, making it easier for editors and readers to understand how decisions are made.
2. Human‑in‑the‑Loop Audits – Quarterly reviews involve a cross‑functional team that evaluates flagged stories for potential bias or misinformation.
3. Reader Feedback Loops – The newsroom actively solicits feedback from its audience, ensuring that the AI adapts to changing community needs.

These practices mirror industry‑wide calls for “AI ethics in journalism,” a theme that the article ties back to a broader policy discussion featured in a linked editorial from the Newspaper Association of America.

4. The Human Touch: Stories That AI Missed

Mitchell highlights several instances where human intuition outshone algorithmic predictions. One memorable case involved a local environmental protest. The AI, trained on historical data that favored economic news over environmental coverage, initially down‑ranked the story. It was only after a vigilant reporter noticed the rising chatter on community forums that the piece was elevated and subsequently became a top‑story of the day.

These stories reinforce Mitchell’s point that “people decide what matters.” While AI can surface trending topics, editors ultimately determine which narratives are newsworthy, which require deeper investigation, and which might be better suited for a different platform (e.g., a community blog rather than a front‑page article).

5. Looking Ahead: AI as an Editor’s Assistant

The article ends on an optimistic note, with Mitchell outlining the future of AI in the newsroom. The Union Leader plans to experiment with natural‑language generation to draft initial story outlines, thereby freeing up reporters to focus on investigative work. However, he stresses that any AI‑generated content will undergo rigorous editorial review before publication.

Mitchell also envisions a more collaborative ecosystem, where the AI learns from both editorial decisions and reader interactions in real time. By incorporating feedback mechanisms, the system could become increasingly attuned to the community’s evolving priorities—whether that means highlighting a surge in COVID‑19 cases, tracking local school board reforms, or spotlighting a cultural festival.

6. Broader Implications for the Industry

The article contextualizes the Union Leader’s experience within a larger industry shift. According to a linked study by the American Press Institute on “AI in Journalism,” small and mid‑size newspapers are uniquely positioned to experiment with AI because they often have tighter budgets but a more engaged local readership. Mitchell’s narrative serves as a case study for how AI can coexist with, rather than replace, traditional editorial practices.

The piece concludes with a reflection on the ethical responsibility that comes with leveraging AI in journalism: “When you’re filtering data, you’re deciding who hears what. That’s a powerful position, and it’s one that requires humility, transparency, and a relentless commitment to the public good.”

7. Key Takeaways

• Hybrid Workflow: The Union Leader uses AI to surface potential stories, but editors curate the final selection.
• Bias Mitigation: Transparency, human audits, and reader feedback help keep the AI in check.
• Human Judgment Matters: Many important stories were identified by human intuition, not AI alone.
• Future Vision: AI will assist in drafting, but the editorial team will remain the final gatekeeper.
• Industry Relevance: Small newspapers can lead the way in responsibly integrating AI into journalism.

The article’s final paragraph—paired with a photo of Mitchell at his desk surrounded by both physical newspapers and a laptop—encapsulates the union of old and new. “We’re not replacing the editor with a robot,” Mitchell says, “we’re giving them a better tool.”

In an era where algorithms can dictate what we see, the Union Leader’s story reminds us that people still wield the ultimate power: to decide what matters.