Recent years have witnessed an interesting phenomenon in which users come together to interrogate potentially harmful algorithmic behaviors they encounter in their everyday lives. Researchers have started to develop theoretical and empirical understandings of these user-driven audits, with a hope to harness the power of users in detecting harmful machine behaviors. However, little is known about users’ participation and their division of labor in these audits, which are essential to support these collective efforts in the future. Through collecting and analyzing 17,984 tweets from four recent cases of user-driven audits, we shed light on patterns of users’ participation and engagement, especially with the top contributors in each case. We also identified the various roles users’ generated content played in these audits, including hypothesizing, data collection, amplification, contextualization, and escalation. We discuss implications for designing tools to support user-driven audits and users who labor to raise awareness of algorithm bias.

Algorithmic systems help manage the governance of digital platforms featuring user-generated content, including how money is distributed to creators from the profits a platform earns from advertising on this content. However, creators producing content about disadvantaged populations have reported that these kinds of systems are biased, having associated their content with prohibited or unsafe content, leading to what creators believed were error-prone decisions to demonetize their videos. Motivated by these reports, we present the results of 20 interviews with YouTube creators and a content analysis of videos, tweets, and news about demonetization cases to understand YouTubers' perceptions of demonetization affecting videos featuring disadvantaged or vulnerable populations, as well as creator responses to demonetization, and what kinds of tools and infrastructure support they desired. We found creators had concerns about YouTube's algorithmic system stereotyping content featuring vulnerable demographics in harmful ways, for example by labeling it "unsafe'' for children or families -- creators believed these demonetization errors led to a range of economic, social, and personal harms. To provide more context to these findings, we analyzed and report on the technique a few creators used to audit YouTube's algorithms to learn what could cause the demonetization of videos featuring LGBTQ people, culture and/or social issues. In response to the varying beliefs about the causes and harms of demonetization errors, we found our interviewees wanted more reliable information and statistics about demonetization cases and errors, more control over their content and advertising, and better economic security.