X's 'open source' algorithm isn't a win for transparency, researchers say

"When X's engineering team published the code that powers the platform's "for you" algorithm last month, Elon Musk said the move was a victory for transparency. "We know the algorithm is dumb and needs massive improvements, but at least you can see us struggle to make it better in real-time and with transparency," Musk wrote. "No other social media companies do this.""

"The code, much like an earlier version published in 2023, is a "redacted" version of X's algorithm, according to John Thickstun, an assistant professor of computer science at Cornell University. "What troubles me about these releases is that they give you a pretense that they're being transparent for releasing code and the sense that someone might be able to use this release to do some kind of auditing work or oversight work," Thickstun told Engadget. "And the fact is that that's not really possible at all.""

"Predictably, as soon as the code was released, users on X began posting lengthy threads about what it means for creators hoping to boost their visibility on the platform. For example, one post that was viewed more than 350,000 times advises users that X "will reward people who conversate" and "raise the vibrations of X." Another post with more than 20,000 views claims that posting video is the answer. Another post says that users should stick to their "niche" because "topic switching hurts your reach.""

X released code for its "for you" recommendation algorithm and framed the publication as a transparency victory. The published code is a redacted version similar to an earlier 2023 release, and researchers say it does not provide the level of detail needed to understand platform behavior or enable effective auditing in 2026. Cornell assistant professor John Thickstun called the releases a pretense of transparency that cannot support oversight. After the release, users posted threads proposing tactics to boost visibility—encouraging conversation, posting video, and staying in a niche—but researchers warn those inferences are unreliable.

Read at Engadget