DOGE may be using an algorithm to fire federal workers

In the past month and a half, the Trump Administration has drastically reduced the federal government workforce.

The cuts alone have generated concern and anger among workers and those who rely on US government services. Adding to the angst: a new concern that government employees could be fired by an algorithm, as engineers modify a legacy reduction-in-force (RIF) software program to assist in their efforts, according to Abigail Kunkler, a law fellow with the nonprofit Electronic Privacy Information Center (EPIC).

Kunkler referenced a February article by Wired citing unnamed sources who told it the unofficial Department of Government Efficiency (DOGE) was retooling AutoRIF software to assist in deciding which employees to lay off. (Wired’s sources reported that most layoffs to that point had been determined manually.)

The day after the article was published, the US Office of Personnel Management ordered agencies to submit RIF plans and file them with the Office of Management and Budget (OMB). 

While not an actual federal department, DOGE is a government entity created by President Donald J. Trump with the self-stated mission of reducing waste, fraud, and abuse. To date, DOGE’s efforts have affected 18 federal agencies with layoffs or buyouts. While the exact number of federal job cuts in 2025 remains unclear, reports estimate there have been roughly 222,000 layoffs so far, with more expected as agencies implement budget cuts.

Driven by the government cuts, US layoffs surged 245% in February, according to Reuters.

“It is not clear how AutoRIF has been modified or whether AI is involved in the RIF mandate (through AutoRIF or independently),” Kunkler wrote in a blog post. “However, fears of AI-driven mass-firings of federal workers are not unfounded. Elon Musk and the Trump Administration have made no secret of their affection for the dodgy technology and their intentions to use it to make budget cuts. And, in fact, they have already tried adding AI to workforce decisions.”

Proponents of automated decision-making software claim it improves efficiency and reduces risks of mismanagement and discrimination. However, its use raises concerns about bias, surveillance, and lack of transparency, Kunkler said. The tools often perpetuate bias due to flawed information, such as incomplete or discriminatory historical data, and can lead to arbitrary or discriminatory decisions, potentially violating workers’ rights and laws like Title VII of the Civil Rights Act of 1964.

The creep of worker data collection, surveillance, rating systems, and automated decision-making is called “algorithmic management.” DOGE’s attempts to use a large language model (LLM) to cull “unnecessary” workers is a form of algorithmic management and automated decision-making, Kunlkler said.  

AutoRIF, developed by the Department of Defense more than 20 years ago, helps agencies manage workforce reductions. Wired reported that DOGE operatives have been editing its code, with updates made recently through a repository in the Office of Personnel Management’s GitHub, managed by Musk associates after Trump took office. However, a review of that GitHub site contained no “public” repositories.

Efforts to contact and/or get comment from the White House, DOGE, the DOGE Caucus or the Office of Personnel Management were unsuccessful.

“Federal employers using automated decision-making tools sharply reduces transparency for workers and their representatives,” Kunkler said. “There is often no insight into how the tool works, what data it is being fed, or how it is weighing different data in its analysis. The logic behind a given decision is not accessible to the worker and, in the government context, it is near impossible to know how or whether the tool is adhering to the statutory and regulatory requirements a federal employment tool would need to follow.”