Does This Button Work? Investigating YouTube's Ineffective User Controls

This paper presents a large-scale experimental audit of YouTube's user control mechanisms for managing unwanted video recommendations. Drawing on crowdsourced data from 22,722 participants using a custom-built browser extension, we analyzed over 567 million recommendations over six months. The extension introduced a ''Stop Recommending'' button overlaying recommended videos, which—depending on randomized assignment—triggered one of four native feedback signals to YouTube (e.g., ''Dislike,'' ''Not Interested,'' ''Don't Recommend Channel,'' ''Remove from History'') or no signal at all in the control group. This design allowed us to assess the effectiveness of different user controls through actual user behavior and downstream changes in recommendations. Using a machine learning model trained to estimate video similarity, we quantified how often unwanted content reappeared after user feedback. We find that YouTube's feedback mechanisms are largely ineffective: even the most ''definitive'' controls prevented fewer than half of similar recommendations. Since unwanted recommendations are relatively rare, our large-scale approach was essential to detect these effects. These findings reveal a substantial gap between user expectations and platform behavior. We conclude with design and policy recommendations to enhance user agency, transparency, and researcher access for platform accountability.