While artificial intelligence appears to be a shiny new bauble full of promises and perils, lawmakers in both parties acknowledge that they must first resolve a less trendy but more fundamental problem: data privacy and protection. With dozens of hearings on data privacy held in the past five years, lawmakers in both chambers have proposed several bills, but Congress has enacted no federal standard as dickering over state-preemption has stymied any advances, Roll Call reports. “I agree that data privacy is going to be the foundation of that trust that everyone believes” is essential to widespread adoption of AI systems, Sen. John Hickenlooper, D-Colo., said in an interview. “We have got to know who owns what data and that the use of data is not harmful.” Hickenlooper, chairman of the Senate Commerce Subcommittee on Consumer Protection, Product Safety, and Data Security, said full committee Chair Maria Cantwell, D-Wash., “feels the same commitment to making sure that at some point, perhaps not this year, but with a sense of urgency, we get a data privacy bill because that is going to underpin so much of what’s going to happen in terms of creating trust in AI.”
Rep. Cathy McMorris Rodgers, R-Wash., who chairs the House Energy and Commerce Committee, echoed those sentiments when she addressed the fifth annual Data Privacy Conference USA in Washington, D.C., last week. “As CEOs travel to Washington, D.C., to discuss issues like artificial intelligence, I worry lawmakers might lose focus on what should be the foundation for any AI efforts, which is establishing comprehensive protections on the collection, transfer and the storage of our data,” Rodgers told the gathering. Rodgers advocates legislation that would impose data minimization provisions that would “prohibit Big Tech from collecting your data without limit so they can feed it to their AI algorithms,” Kelly said. Generative AI technologies are trained on extremely large quantities of data scoured from the internet, including text, images, video and audio generated by users as well as public and private institutions. The AI models then learn the patterns in such data, enabling them to generate new content in response to queries that match the patterns observed in the training data.