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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. 

Detecting and avoiding obstacles while navigating can pose a challenge for people with low vision, but augmented reality (AR) has the potential to assist by enhancing obstacle visibility. Perceptual and user experience research is needed to understand how to craft effective AR visuals for this purpose. We developed a prototype AR application capable of displaying multiple kinds of visual cues for obstacles on an optical see-through head-mounted display. We assessed the usability of these cues via a study in which participants with low vision navigated an obstacle course. The results suggest that 3D world-locked AR cues were superior to directional heads-up cues for most participants during this activity.