When an individual is harmed by someone in power, such as a workplace manager, it can help to identify allies--people who would offer sympathy, 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 potentially exacerbate the harm. We examine this problem in the specific context of PhD students navigating advising challenges and present a social media platform called "Moa" that brings together a number of features that we believe facilitate 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 anonymity--neither senders nor recipients learn each other's identities, even as the post reaches the right audience. A 3-week field study with 47 real-world users showed that the features in combination facilitated sensitive conversations about advising, with 22.6% of users using consent boundaries. We discuss both our overall "recipe" for systems for ally discovery and the benefits of a consent-centered approach to design.

翻译：当个人受到掌权者（如工作场所主管）的伤害时，识别盟友——即愿意提供同情、建议或支持行动的人——可能会有所帮助。然而，盟友发现充满风险，因为最相关的人（例如向同一主管汇报的人）可能缺乏同情心，甚至可能加剧伤害。我们以博士生应对指导挑战这一特定情境为背景研究此问题，并介绍一个名为"Moa"的社交媒体平台，该平台整合了我们认为有助于盟友发现的多种功能。Moa最具创新性的元素是一个受众选择过程，它采用我们称之为"同意边界"的机制，允许用户根据共同社会身份或生活经历等因素灵活定义每条帖子或评论的受众范围，同时保持匿名性——发送者和接收者均无法获知对方的身份，即使帖子精准触达目标受众。一项为期3周、包含47名真实用户的实地研究表明，这些功能的组合促进了关于指导问题的敏感对话，其中22.6%的用户使用了同意边界。我们探讨了盟友发现系统的整体"配方"以及以同意为中心的设计方法带来的优势。