Artificial intelligence (AI) is rapidly transforming the media landscape, altering the practice of verifying whether information is accurate in profound, if misunderstood, ways. For decades, fact checking created a bottleneck because it was labor intensive and relied on people manually verifying information using primary sources. Today, AI performs this task at lightning speed, using Large Language Models (LLMs) to read, cross-reference, and predict the veracity of claims in real-time.
This shift is not merely technical; it is economic. AI has the potential to democratize verification by collapsing the cost of the fact-check. While traditional outlets can only verify a fraction of public statements, AI offers the possibility of “Fact-Checking as a Service.” This integration is already in practice in some forums, from xAI’s Grok’s ability to respond to users directly in their feeds, to tools that attempt to “pre-bunk” misinformation before it spreads.
For the first time in history, users may soon have their own personalized fact-checking agents delivering customized, real-time context without waiting for a newsroom to publish a verdict. Studies show that, in most fact checking cases, LLMs are capable of matching human quality.
For the first time in history, users may soon have their own personalized fact-checking agents delivering customized, real-time context without waiting for a newsroom to publish a verdict.
However, we must be realistic about limitations. While AI enhances efficiency, it does not guarantee objectivity. The speed and automated nature of AI open the door to “hallucinations” and errors. More importantly, on sensitive political topics, the concept of a “balanced” AI may be an impossibility.
Consider a subjective query like, “Who was the best U.S. president?” There is no objective way to answer this; the result depends entirely on the values weighted by the model or the user. Similarly, in the fog of breaking news, authoritative sources often do not exist yet. Whether the fact checker is human or silicone, consensus falls apart when data is scarce or the debate is ideological.
This is where the human element remains distinct. AI tests in fields like medicine show that while the technology can match human performance in routine analysis, it fails to outperform top-tier experts in complex, high-stakes diagnoses. This helps to illustrate how the ideal future of fact verification within the media is not AI replacing humans, but a “human-in-the-loop” system where AI handles the scale, and humans supply the nuance. We see early signs of this in systems like X’s, where AI is used not to dictate truth, but to enhance and amplify the work of human fact-checkers.
Given these nuances, how should policymakers approach regulation? The answer is with extreme caution.
As Washington turns its eye toward AI regulation, we must establish a fundamental principle: AI design and outputs are forms of expression, should be considered speech, and afforded the highest level of protection under the First Amendment. The act of writing code, which is used to create LLMs, is effectively the editorial judgment of its creators. Any law that restricts the ability of developers to build these tools, or restricts users from receiving their outputs, risks violating core constitutional principles. While narrow exceptions for defamation or libel may apply, the government should not be in the business of regulating the “truthfulness” of an AI’s output any more than it regulates the editorial page of a newspaper.
The government should not be in the business of regulating the “truthfulness” of an AI’s output any more than it regulates the editorial page of a newspaper.
Some policymakers, recognizing this First Amendment hurdle, have pivoted toward “transparency” mandates—forcing companies to reveal how their models work. While well intentioned, this is a dangerous path. Requiring that a tech firm publish its algorithm is equivalent to forcing Coca-Cola to publish its recipe, or Google to reveal its search ranking signals. These are trade secrets; exposing them destroys the core innovation of the business and allows bad actors to “game” the system.
A better alternative is to trust the market. If an LLM consistently displays a political slant, users will diagnose it, screenshot it, and share it. In a competitive market, this creates pressure. If a model is uselessly biased, users can switch to a competitor.
We are moving toward a future where users may “silo” themselves by using LLMs that reflect their own worldviews. While some may view this as a flaw of human psychology, it is not a problem that government regulation can—or should—solve. Transparency should be a contract between the user and the company, driven by consumer demand, not a compliance checklist enforced by the state.
Ultimately, AI can dramatically improve the efficiency and accuracy of the fact checking industry. But to realize that promise, we must resist the urge to centralize control of AI. Users must be diligent, demanding, and willing to experiment with different models. If we allow the market to optimize, we will find that the best defense against misinformation is not a government regulator, but a competitive ecosystem of AI tools that empower people to find the truth.
Spence Purnell is a resident senior fellow with the R Street Institute’s technology and innovation team.