Fact-Checking in the Age of AI

TL;DR: An unconfirmed account of newsroom AI practices spread through secondary coverage and became accepted as fact. This moment illustrates how quickly accuracy erodes when verification fails and amplification takes over.

This piece is prompted by a widely circulated article about McClatchy, one of the country's largest newspaper chains, and its internal discussions about expanding the use of artificial intelligence in newsrooms.

The article reported that McClatchy has shuttered its Washington bureau, laid off staff, and discussed publishing AI-generated stories without human review. It also described proposals that raised concerns about using AI-generated audio that would replicate reporters' voices for podcasts. Union leaders who expected a routine contract conversation instead left the meeting alarmed by how far management appeared willing to push automation.

Whether every element of that proposal is ultimately implemented is secondary to what the discussion represents. A major news organization is openly exploring practices that reach into the core obligations of journalism.

Accuracy sits at the center of those obligations.

Fact-checking is a responsibility attached to publishing. Once information enters the public record, it shapes understanding and decision-making. Errors spread quickly, persist over time, and often outpace corrections. The cost of getting something wrong extends beyond embarrassment. It affects trust, credibility, and real people.

Verification confirms names, dates, sources, and context. It clarifies what is known and what remains uncertain. It also creates accountability. Someone stands behind the work and addresses mistakes when they occur.

Artificial intelligence complicates this environment.

AI tools can summarize documents, draft copy, and reproduce professional tone at speed. They generate language that sounds confident and complete. They do not independently assess truth or relevance.

These systems produce text based on patterns in existing material. When those patterns include outdated reporting, inaccurate claims, or repeated errors, the output reflects those same flaws. The presentation can appear polished while remaining incorrect.

This introduces risk for news organizations under pressure.

Financial strain and reduced staffing make efficiency attractive. Automation promises faster output and broader coverage. At the same time, public confidence in news organizations remains fragile. Removing or minimizing human review weakens the process that underpins credibility.

Fact-checking requires judgment and context. It requires understanding why a detail matters and how it might be misread. It also requires ownership when something goes wrong.

AI can support journalism when used carefully. It can assist with research, organization, and background work. Responsibility for accuracy must remain with people who understand the consequences of publication.

Errors made at scale move quickly. They spread confidently and repeat easily. They do not correct themselves.

Fact-checking introduces necessary friction. That friction protects readers and the integrity of the outlet. It slows publication in service of accuracy and trust.

As tools evolve, standards remain. The obligation to verify does not change with technology.

Fact-checking still matters because accuracy still matters. That principle applies regardless of how stories are produced or which tools are involved.

AI-generated image. Not representative of real individuals or events.