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In recent years, there has been a surge of concern about the massive amounts of data that governments collect about us. Calls for transparency and accountability have never been louder, especially when it comes to the ways that this data is fed into government AI systems to help “train” them to make decisions that directly but often secretly impact our lives—“from facial recognition and autonomous weapons to criminal risk assessments and public benefits administration,” as scholars Kate Crawford and Jason Schultz have put it.

There is growing consensus that a necessary if insufficient component of any effective regulatory regime for AI is greater access to and understanding of these training datasets that serve as “ground truth” for machine learning. But AI regulation in the United States is still in its “early days.” While we wait for the federal and state governments to get their acts together on AI, we’re left with the familiar, flawed tools of government transparency: freedom of information laws (FOILs). Although FOILs have come under sustained and legitimate criticism in recent years, two recent court decisions demonstrate the role that FOILs can continue to play in this age of big data, machine learning, and AI to compel meaningful public oversight of government-held data—what data governments are collecting, what they are doing with the data, and what decisions, automated or otherwise, they are making based on this data.