Bipartisan bill seeks to curb recommendation algorithms

A bipartisan group of House lawmakers has introduced legislation that would give people more control over the algorithms that shape their online experience. If passed, the Filter Bubble Transparency Act would require companies like Meta to offer a version of their platforms that runs on an “input-transparent” algorithm that doesn’t pull on user data to generate recommendations.

The bill would not do away with “opaque” recommendation algorithms altogether but would make it a requirement to include a toggle that allows people to switch that functionality off. Additionally, platforms that continue to use recommendation algorithms need to have a notification that informs people those recommendations are based on inferences generated by their personal data. The prompt can be a one-time notice, but it would need to be presented in a “clear, conspicuous manner,” according to the proposed bill.

The legislation was introduced by Representatives Ken Buck (R-CO), David Cicilline (D-RI), Lori Trahan (D-MA) and Burgess Owens (R-UT). It’s a companion bill to legislation Senators John Thune of South Dakota and Richard Blumenthal of Connecticut introduced this past June. “Consumers should have the option to engage with internet platforms without being manipulated by secret algorithms driven by user-specific data,” Buck told Axios, the first outlet to report on the legislation.

Lawmakers have frequently criticized social media giants for using recommendation algorithms to boost user engagement, but so far, there’s been little legislative action to curb their use. In the aftermath of the January 6th US Capitol attack, a group of more than 30 Democratic lawmakers called on Meta (then known as Facebook), Twitter and YouTube to make substantive changes to their recommendation engines but ultimately stopped short of threatening regulatory action. Although the Filter Bubble Transparency Act has bipartisan support across the House and Senate, it’s unclear if it would pass.