SoundsideAs companies run out of training data, regulators want more guardrails for AI
Each year we can count on the reliable passing of the seasons: the leaves falling in Autumn, winter blooming into spring, and if you’re following tech, the reliable attempt – yet again – at a federal data privacy bill in the U.S. Congress.
Back in April, two Washington legislators – Democrat Senator Maria Cantwell and Republican Representative Cathy McMorris Rodgers – announced to much fanfare that they would be leading the charge this year with newly drafted legislation called the American Privacy Rights Act.
"Researchers project that if current trends continue, companies training large language models may run out of new publicly available high-quality data to train AI systems as early as 2026," said Senator Cantwell in a hearing on the issue earlier this month. "So without a strong privacy law, when the public data runs out, nothing stopping it from using our private data..."
As AI systems and their developers are hungry for more data, companies are starting to ask consumers to provide it -- often without ethical guidelines in place.
In the hearing, Senator Cantwell called AI advancements “...fuel on a campfire in the middle of a windstorm," citing concerns with deepfakes, data security, price gauging, and data transparency.
"Privacy is not a partisan issue," said Senator Cantwell. "According to Pew Research, the majority of Americans across the political spectrum want more support for regulation. I believe our most important private data should not be bought or sold without our approval..."
Will concerns driven by AI be push tech regulation to the finish line?
To hear more about this latest attempt and if this effort might make it through, Soundside’s Libby Denkmann caught up with AI researchers Jacob Metcalf and Jennifer King.
Guests:
- Dr. Jacob Metcalf is a researcher at the nonprofit Data & Society, where he studies and consults with companies on ethical AI practices.
- Dr. Jennifer King is a privacy and Data Policy Fellow at the Stanford Institute for Human Centered Artificial Intelligence.
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