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.

Biomorphs, Richard Dawkins's iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations can fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems.