When an individual is harmed by someone in power, such as a workplace manager, it can help to identify allies—people who would offer advice or supportive action. However, ally discovery is fraught because the very people who might be most relevant—e.g., someone who reports to the same manager—might not be sympathetic and could exacerbate the harm. We examine this problem in the context of PhD students navigating advising challenges and present a social media platform called “Moa” that brings together a number of features for facilitating ally discovery. Moa’s most novel element is an audience selection process that uses what we call consent boundaries, which allow users to flexibly define each post or comment’s audience based on factors such as common social identity or lived experience, all while preserving mutual anonymity. A 3-week field study with 47 real-world users showed that Moa’s features in combination facilitated sensitive conversations about advising, with 7 out of 31 posters (22.6%) using consent boundaries. We discuss both our overall “recipe” for systems for ally discovery and the benefits of a consent-centered approach to design.
2026-10-01
2026-06-24