Palantir and Credal AI: US Health Department Filters Grants for 'Correctness'

Imagine you're submitting an application for a scientific grant, having spent months on research and calculations. But your work is rejected in fractions of a second not because the mathematics is flawed, but because an algorithm found "incorrect" words in your text. This is exactly what's happening right now in the corridors of the U.

S. Department of Health and Human Services (HHS). Since March 2025, the agency has officially put neural networks on guard over ideological purity, using Palantir's infrastructure and tools from startup Credal AI.

This event is not just another piece of news about software implementation. It's a tectonic shift in how the state interacts with science and society. Previously, checks for conformity with political directives were a slow, human affair.

Now, Palantir, a company whose image is inextricably linked to Peter Thiel and state surveillance, has provided the infrastructure for automated censorship. Working alongside them is Credal AI—a startup that specializes in securely implementing language models into corporate processes. Together, they've created the perfect filter for purging a particular agenda from the state budget.

Two acronyms and one broad concept have come under fire: DEI (diversity, equity, and inclusion) and so-called "gender ideology." If your health program accounts for the specifics of minorities or uses terminology that the current department leadership considers excessive, the chances of securing funding approach zero. The algorithm doesn't consider context—it searches for markers flagged as "undesirable" in the current political cycle.

To understand the scale, it's worth recalling what Palantir did before. Their Foundry platform spent years helping intelligence agencies connect disparate data into a unified picture to identify threats or analyze supply chains. The retooling of this power to search for "social engineering" in grants looks like a logical, if frightening, development of control technologies.

If AI was previously often accused of a liberal bias, we now see the pendulum swing with a bang to the opposite side, transforming into an instrument of conservative revision.

The problem here isn't just politics, but the very nature of large language models. We know that neural networks can make mistakes and misinterpret text. When such a model becomes a judge determining the fate of millions of dollars, process transparency disappears.

How exactly did Credal AI configure its filters? What weights were assigned to words like "equality" or "gender"? Answers to these questions are hidden behind corporate secrecy and department classification marks.

For the scientific community, this signals the beginning of an era of self-censorship. Researchers whose work depends vitally on grants will now think twice before using certain vocabulary in their work. This creates a dangerous precedent: science begins adapting to an algorithmic filter configured for current circumstances.

Today it's fighting DEI, but what will neural networks be ordered to search for tomorrow? This case clearly shows that the era of "neutral AI" has ended before it even began. Technologies have become an extension of the will of those who hold the budget lever.

The use of Palantir in this scheme only confirms: effective data management in the modern world is first and foremost effective filtering of unwanted meanings.

Key takeaway: Will the "ideological AI filter" become standard across all government agencies, or will we see large-scale legal resistance from the scientific community?