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Can AI help DOGE slash government budgets? It’s complex.

AiNEWS2025 by AiNEWS2025
2025-02-12
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The stated goal of DOGE’s actions, per a statement from a White House spokesperson to the New York Times on Thursday, is “slashing waste, fraud, and abuse.”

As I point out in my story published Friday, these three terms mean very different things in the world of federal budgets, from errors the government makes when spending money to nebulous spending that’s legal and approved but disliked by someone in power. 

Many of the new administration’s loudest and most sweeping actions—like Musk’s promise to end the entirety of USAID’s varied activities or Trump’s severe cuts to scientific funding from the National Institutes of Health—might be said to target the latter category. If DOGE feeds government data to large language models, it might easily find spending associated with DEI or other initiatives the administration considers wasteful as it pushes for $2 trillion in cuts, nearly a third of the federal budget. 

But the fact that DOGE aides are reportedly working in the offices of Medicaid and even Medicare—where budget cuts have been politically untenable for decades—suggests the task force is also driven by evidence published by the Government Accountability Office. The GAO’s reports also give a clue into what DOGE might be hoping AI can accomplish.

Here’s what the reports reveal: Six federal programs account for 85% of what the GAO calls improper payments by the government, or about $200 billion per year, and Medicare and Medicaid top the list. These make up small fractions of overall spending but nearly 14% of the federal deficit. Estimates of fraud, in which courts found that someone willfully misrepresented something for financial benefit, run between $233 billion and $521 billion annually. 

So where is fraud happening, and could AI models fix it, as DOGE staffers hope? To answer that, I spoke with Jetson Leder-Luis, an economist at Boston University who researches fraudulent federal payments in health care and how algorithms might help stop them.

“By dollar value [of enforcement], most health-care fraud is committed by pharmaceutical companies,” he says. 

Often those companies promote drugs for uses that are not approved, called “off-label promotion,” which is deemed fraud when Medicare or Medicaid pay the bill. Other types of fraud include “upcoding,” where a provider sends a bill for a more expensive service than was given, and medical-necessity fraud, where patients receive services that they’re not qualified for or didn’t need. There’s also substandard care, where companies take money but don’t provide adequate services.

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