A recent study by George Washington University revealed that political pressure can polarize members of the Federal Reserve during critical rate-setting decisions, even in a simulated environment using artificial intelligence. The researchers recreated a Federal Open Market Committee (FOMC) meeting using AI agents modeled after real-life policymakers, taking into account their historical policy stances, speeches, and biographies. These AI agents processed real-time economic data and financial news to reach decisions. The simulation, which mirrored the July 2025 FOMC meeting, showed that under political pressure, the AI agents became fragmented, and dissent among board members increased.

According to Sophia Kazinnik and Tara Sinclair, the study demonstrates that the Federal Reserve is not completely insulated from politics. “Outside scrutiny can shape internal decision-making, even in an institution guided by formal rules,” the researchers noted. While central banks are not yet using AI to directly set monetary policy, many are experimenting with the technology to improve operations. The Fed has explored generative AI for analyzing meeting minutes, the European Central Bank uses machine learning to forecast inflation, and the Bank of Japan applies AI to deepen economic analyses. Australia’s central bank is testing an AI tool that summarizes policy-related research, though Governor Michele Bullock emphasized that AI is used solely to improve efficiency, not to make policy decisions.

Despite growing experimentation, many central banks remain in the early stages of AI adoption, focusing on ensuring proper governance and high-quality data, according to a report by the Bank for International Settlements. The study highlights the increasing role of AI in financial decision-making and the continuing influence of politics on even highly structured institutions like the Federal Reserve.