Last Wednesday, US lawmakers introduced a new bill that represents one of the country’s first major efforts to regulate AI. There are likely to be more to come.

It hints at a dramatic shift in Washington’s stance toward one of this century’s most powerful technologies. Only a few years ago, policymakers had little inclination to regulate AI. Now, as the consequences of not doing so grow increasingly tangible, a small contingent in Congress is advancing a broader strategy to rein the technology in.

Though the US is not alone in this endeavor—the UK, France, Australia, and others have all recently drafted or passed legislation to hold tech companies accountable for their algorithms—the country has a unique opportunity to shape AI’s global impact as the home of Silicon Valley. “An issue in Europe is that we’re not front-runners on the development of AI,” says Bendert Zevenbergen, a former technology policy advisor in the European Parliament and now a researcher at Princeton University. “We’re kind of recipients of AI technology in many ways. We’re definitely the second tier. The first tier is the US and China.”

The new bill, called the Algorithmic Accountability Act, would require big companies to audit their machine-learning systems for bias and discrimination and take corrective action in a timely manner if such issues were identified. It would also require those companies to audit not just machine learning but all processes involving sensitive data—including personally identifiable, biometric, and genetic information—for privacy and security risks. Should it pass, the bill would place regulatory power in the hands of the US Federal Trade Commission, the agency in charge of consumer protection and antitrust regulation.

The draft legislation is the first product of many months of discussion between legislators, researchers, and other experts to protect consumers from the negative impacts of AI, says Mutale Nkonde, a researcher at the Data & Society Research Institute who was involved in the process. It comes in response to several high-profile revelations in the past year that have shown the far-reaching damage algorithmic bias can have in many contexts. These include Amazon’s internal hiring tool that penalized female candidates; commercial face analysis and recognition platforms that are much less accurate for darker-skinned women than lighter-skinned men; and, mostly recently, a Facebook ad recommendation algorithm that likely perpetuates employment and housing discrimination regardless of the advertiser’s specified target audience.