Concerns Grow Over Algorithmic Management in Federal Layoffs
In the past month and a half, the Trump Administration has made significant reductions to the federal government workforce.
The impact of these cuts alone has 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.
The Rise of Algorithmic Management
Abigail Kunkler, a law fellow with the nonprofit Electronic Privacy Information Center (EPIC), has expressed concerns over the use of algorithmic management in federal layoffs. According to her, the unofficial Department of Government Efficiency (DOGE) is retooling AutoRIF software to assist in deciding which employees to lay off.
AutoRIF, developed by the Department of Defense more than 20 years ago, helps agencies manage workforce reductions. However, a review of the GitHub site managed by Musk associates after Trump took office revealed no "public" repositories containing updates made recently through that repository.
The Fear of AI-Driven Mass-Firings
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.
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.
The Impact on Federal Employees
Driven by the government cuts, US layoffs surged 245% in February, according to Reuters. The exact number of federal job cuts in 2025 remains unclear, with reports estimating there have been roughly 222,000 layoffs so far.
Federal employers using automated decision-making tools sharply reduces transparency for workers and their representatives. 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.
Concerns Over Bias and Surveillance
The tools often perpetuate bias due to flawed information, such as incomplete or discriminatory historical data, and can lead to arbitrary or discriminatory decisions. This could potentially violate 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.
Efforts to Contact the White House Were Unsuccessful
Efforts to contact and/or get comment from the White House, DOGE, the DOGE Caucus or the Office of Personnel Management were unsuccessful. The lack of transparency in government agencies' use of automated decision-making tools raises serious concerns.
The future of federal layoffs and algorithmic management remains uncertain. However, one thing is clear: the impact on federal employees and those who rely on US government services will be significant.