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Artificial intelligence sits at the center of nearly every major debate, from jobs and data centers to healthcare, national security and consumer protection. Missing from the national spotlight, however, is a more fundamental question: Will AI models pursue the truth, or will they be permitted to bury undisclosed biases inside their responses?
On the surface, general-purpose AI tools present themselves as neutral sources of information and analysis, capable of answering most questions with citations, well-reasoned explanations and unbiased feedback.
But reality paints a different picture. While it is easy to spot and dismiss the most egregious violations of neutrality and accuracy (e.g., depictions of the Founding Fathers as African American or British medieval kings as racially diverse), underlying biases are not as readily apparent. The average user is much less likely to detect when a model is steering them toward an engineered outcome or framing a response through a politically skewed lens, as data shows most users do not fact-check answers from AI models.
Reporting and research are beginning to shine a light on the existence of these undisclosed biases. The Washington Post, for instance, tested the leading models on hot-button political questions and found that they consistently favored left-leaning arguments while presenting those positions as neutral.
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MIT’s Center for Constructive Communication reached similar conclusions, documenting that reward models display left-leaning biases even when trained on truthful statements, with strong bias on topics like climate, energy and labor unions.
At the state level, ambitious lawmakers are exploiting the absence of federal preemption to pursue codifying these hidden biases. In New York and California, legislators are seeking to advance bills akin to Colorado’s original Artificial Intelligence Act, which would impose impact assessments and anti-discrimination mandates.
As proposed, these frameworks would create structural incentives for companies to alter or omit information from responses to avoid legal liability. The FTC’s own reading of Colorado’s AI law is that it “appears to coerce companies into altering the output of their AI models” to advance the state’s ideological objectives. In other words, politicians are attempting to enact laws that, in practice, penalize accurate model responses.
These biases are not trivial or immaterial. AI is being adopted at a scale faster than any technology in history. Millions of Americans rely on it to find information, complete their work and seek advice. Many are even using it to make sense of our current political environment.
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A recent New York Times report highlighted how voters are increasingly using AI chatbots to determine which candidate to support. The Times noted, “Voters are turning to new AI tools to serve as nonpartisan researchers, viewing them as a viable alternative to traditional news coverage, voter guides or social media.”
While these systems can make political engagement more accessible, a model’s undisclosed ideological leanings can filter information through a biased lens and generate a seemingly neutral answer that is capable of shifting voter opinion on a candidate or policy topic.
At scale, these hidden biases cease to be a harmless quirk or unintended outcome. Instead, they become capable of materially influencing matters central to Americans’ personal, professional, and civic life.
Enter President Donald Trump. Under his leadership, the Trump administration is actively confronting these concerns. The historic AI Action Plan made clear that American AI must be trustworthy and “designed to pursue objective truth rather than social engineering agendas when users seek factual information or analysis.” Subsequent executive orders also blocked federal procurement of biased AI models and ensured that model accuracy is governed under a federal framework.
These biases are not trivial or immaterial. AI is being adopted at a scale faster than any technology in history. Millions of Americans rely on it to find information, complete their work and seek advice. Many are even using it to make sense of our current political environment.
Pursuant to the order that created a national AI framework, FTC Chairman Andrew Ferguson issued a proposed policy statement applying existing consumer protection law to undisclosed model bias.
As the FTC outlines, under Section 5 of the FTC Act, a representation or omission is deceptive if it is likely to mislead a reasonable consumer and is material to that consumer’s decisions. An AI system presented as a neutral source of information while systematically laundering preferred narratives under the guise of objectivity would meet that threshold.
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Ferguson’s policy proposal also reasserts federal jurisdiction. AI models are standardized products designed for nationwide sale, not localized adaptations. However, the current 50-state patchwork allows the most aggressive state legislatures to effectively set the national standard, as complying with the most burdensome regime becomes the most cost-effective. The FTC’s solution applies a single federal standard for AI models that is no different from the way we already regulate products sold nationally, like automobiles and pharmaceuticals.
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This is a commonsense proposal: if a model carries a hidden bias or steers a user contrary to his reasonable expectations, then such alterations must be clearly disclosed to the user, or models risk violating federal consumer protection law.
The American people have a right to know if they are being deceived. The FTC’s proposed policy statement is a necessary step toward fulfilling the mission laid out in the AI Action Plan and advancing American leadership in the AI age.
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