Why Federal Agencies Are Rushing Into AI—And Why That Should Worry Us

There’s a particular kind of excitement happening in Washington right now. It’s the sound of billions in government budgets being redirected toward artificial intelligence. Federal agencies are moving fast, very fast, to implement AI systems across everything from benefits processing to national security decisions. And while I understand the impulse to keep pace with technology, I can’t shake the feeling that we’re watching a high-speed car being driven by people who just got their license last week.

The phrase “trust but verify” keeps coming up in policy circles when people talk about how federal agencies should approach AI adoption. It’s a catchy formula borrowed from Cold War diplomacy, and it sounds reasonable. But here’s what’s bothering me: the “verify” part is dramatically harder than anyone seems willing to admit.

The Pressure to Move Fast

Let’s start with why this is happening at all. The administration has made AI a priority. That’s not inherently wrong—the government absolutely needs to modernize how it operates. But when leadership sets an agenda, institutions scramble to show they’re on board. I’ve seen this pattern repeat itself with every major tech shift: everyone wants to be seen as forward-thinking, so they implement solutions before they fully understand them.

The federal government employs millions of people across dozens of agencies doing complex, consequential work. Imagine you’re a mid-level manager at the Social Security Administration or the IRS. Your leadership is pushing AI. You know your office is drowning in paperwork. An AI system promises to handle routine decisions faster and more efficiently. The pressure to sign that contract is immense. Who wants to be the person who slowed down progress?

This is where things get dangerous. Good intentions meet institutional momentum.

What Does “Verify” Actually Mean?

Here’s the uncomfortable truth that trust-but-verify advocates aren’t quite grappling with: verification requires expertise that most federal agencies don’t have in-house. Testing whether an AI system works correctly isn’t like checking whether software crashes. It’s fundamentally harder because the answers are often ambiguous.

Let’s say an AI system handles benefit claim decisions. How do you verify it’s fair? You need to:

• Test it against historical data to spot bias patterns
• Audit its decisions in real-time to catch problems early
• Maintain human review capacity for complex cases
• Have security teams who understand how the system could be manipulated or attacked
• Regularly retrain or update the system as patterns shift

That’s not a one-time security audit. That’s ongoing, expensive, sophisticated work. And it requires people who understand both government operations and machine learning. Those people are rare, and they typically command higher salaries than federal bureaucracies can offer.

When I talk to technologists about this, they’re honest in ways you don’t always hear in official statements. One data scientist I spoke with, who works on government contracts, told me flatly: “We’re shipping systems we don’t fully understand and hoping we catch problems before they hurt people.” That’s not confidence. That’s a shrug.

The Accountability Question

Here’s what keeps me up at night: who’s responsible when an AI system makes a bad decision that affects a citizen?

If a federal employee processes a claim incorrectly, that’s traceable. There’s a person. There’s a decision-making process you can reconstruct. There’s accountability. But when an algorithm denies someone benefits, or flags them for fraud investigation, or makes some other critical determination—who do you blame? The contractor who built it? The agency that deployed it? The person who supervised its implementation? The math?

The truth is, nobody fully knows. Our legal and bureaucratic systems were designed for human decision-making. They’re creaky when applied to AI.

Trust but verify only works if you can actually trace what went wrong and hold someone accountable. Right now, federal agencies are moving ahead with systems where that chain of accountability is fuzzy at best.

What Should Actually Happen

I’m not arguing the government shouldn’t use AI. That’s unrealistic, and honestly, some applications make genuine sense. But the current approach feels like we’re building the inspection system *after* we’ve already started driving.

What would real verification look like? Federal agencies would need dedicated teams of AI auditors with genuine technical expertise and authority to slow deployments. They’d need mandatory impact assessments for any AI system that affects benefits, security decisions, or civil rights. They’d need transparency requirements so the public can see *how* decisions are being made, not just what the decisions are.

And they’d need to accept that sometimes the answer to “should we implement this AI system?” is “not yet.”

The problem with trust-but-verify in the real world is that trust almost always wins. Verification is slow, tedious, and expensive. Trust is fast and politically convenient. Until we restructure how federal agencies evaluate AI—with actual independent oversight and real consequences for cutting corners—that imbalance will remain.

The agencies rushing forward with AI aren’t evil. They’re not trying to harm anyone. But good intentions don’t catch algorithmic bias. They don’t prevent security breaches. They don’t answer the accountability question. What does is the hard, unglamorous work of genuine verification.

And right now, we’re not doing enough of it.

Source: Why federal agencies need a ‘trust but verify’ AI strategy | Federal News Networ…