Recent work has demonstrated how content moderation practices on social media may unfairly affect marginalized individuals, for example by censoring women's bodies and misidentifying reclaimed terms as hate speech. This study documents and explores the direct experiences of marginalized creators who have been impacted by discriminatory content moderation on Instagram. Collaborating with our participants for over a year, we contribute five co-constructed narratives of discriminatory content moderation from advocates in trauma-informed care, LGBTQ+ sex education, anti-racism education, and beauty and body politics. In sharing these detailed personal accounts, not only do we shed light on their experiences with being blocked, banned, or deleted unfairly, but we delve deeper into the lasting impacts of these experiences to their livelihoods and mental health. Reflecting on their stories, we observe that content moderation on social media is deeply entangled with the situated experiences of offline discrimination. As such, we document how each participant experiences moderation through the lens of their often intersectional identities. Using participatory research methods, we collectively strategize ways to learn from these individual accounts and resist discriminatory content moderation, as well as imagine possibilities for repair and accountability.

This work explores how users navigate the opaque and ever-changing algorithmic processes that dictate visibility on Instagram through the lens of Attachment Theory. We conducted thematic analysis on 1,100 posts and comments on r/Instagram to understand how users engage in collective sensemaking with regards to Instagram’s algorithms, user-perceived punishments, and strategies to counteract algorithmic precarity. We found that the unpredictability in how Instagram rewards or punishes a user can lead to distress, hypervigilance, and a need to appease “the algorithm’’. We therefore frame these findings through Attachment Theory, drawing upon the metaphor of Instagram as an unreliable paternalistic figure that inconsistently rewards users [74]. User experiences are then contextualized through the lens of anxious, avoidant, disorganized, and secure attachment. We conclude by making suggestions for fostering secure attachment towards the Instagram algorithm, by suggesting potential strategies to help users successfully cope with uncertainty.

Machine learning systems are increasingly a part of everyday life, and often used to make critical and possibly harmful decisions that affect stakeholders of the models. Those affected need enough literacy to advocate for themselves when models make mistakes. To understand how to develop this literacy, this paper investigates three ways to teach ML concepts, using linear regression and gradient descent as an introduction to ML foundations. Those three ways include a basic Facts condition, mirroring a presentation or brochure about ML, an Impersonal condition which teaches ML using some hypothetical individual's data, and a Personal condition which teaches ML on the learner's own data in context. Next, we evaluated the effects on learners' ability to self-advocate against harmful ML models. Learners wrote hypothetical letters against poorly performing ML systems that may affect them in real-world scenarios. This study discovered that having learners learn about ML foundations with their own personal data resulted in learners better grounding their self-advocacy arguments in the mechanisms of machine learning when critiquing models in the world.