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.

I always say to my students "you are going to be the future data science leaders of the world". Wherever they end up, I hope they apply critical (Cotter, 2020; Dasgupta & Hill, 2020) and human-centered (Xu, 2019) thinking to the AI decisions they make. As AI algorithms become ever more omnipresent in our lives - from newsfeed organization to product recommendations and beyond - it is our responsibility as educators to equip our students with the necessary tools to interrogate the impacts of AI technology. Luckily, there has been a large push for the integration of ethics into AI curricula. Whether this is in Model AI assignments (Furey & Martin, 2019) or entire conferences (such as FAccT), there is a demand for integrated and critical algorithmic literacies both in the classroom and outside of it. As a social media researcher and AI educator, my work regularly contends with two pillars: Joy and Justice. In this article I intend to outline ways of integrating both play and critical interrogation into AI education, with examples from AI education scholarship (Druga, Vu, Likhith, & Qiu, 2019; Ko et al., 2020) as well as a light experience report highlighting student work. My students join me on this article as they are the main inspiration for innovative joy and justice practices!

I always say to my students "you are going to be the future data science leaders of the world". Wherever they end up, I hope they apply critical (Cotter, 2020; Dasgupta & Hill, 2020) and human-centered (Xu, 2019) thinking to the AI decisions they make. As AI algorithms become ever more omnipresent in our lives - from newsfeed organization to product recommendations and beyond - it is our responsibility as educators to equip our students with the necessary tools to interrogate the impacts of AI technology. Luckily, there has been a large push for the integration of ethics into AI curricula. Whether this is in Model AI assignments (Furey & Martin, 2019) or entire conferences (such as FAccT), there is a demand for integrated and critical algorithmic literacies both in the classroom and outside of it. As a social media researcher and AI educator, my work regularly contends with two pillars: Joy and Justice. In this article I intend to outline ways of integrating both play and critical interrogation into AI education, with examples from AI education scholarship (Druga, Vu, Likhith, & Qiu, 2019; Ko et al., 2020) as well as a light experience report highlighting student work. My students join me on this article as they are the main inspiration for innovative joy and justice practices!

Facebook users interact with algorithms every day. These algorithms can perpetuate harm via incongruent targeted ads, echo chambers, or "rabbit hole" recommendations. Education around the machine learning (ML) behind Facebook (FB) can help users to point out algorithmic bias and harm, and advocate for themselves effectively when things go wrong. One algorithm that FB users interact with regularly is User-Based Collaborative Filtering (UB-CF) which provides the basis for ad recommendation. We contribute a novel research approach for teaching users about a commonly used algorithm in machine learning in real-world context -- an instructive web application using real examples built from the user's own FB data on ad interests. The instruction also prompts users to reflect on their interactions with ML systems, specifically Facebook. In a between-subjects design, we tested both Data Science Novices and Experts on the efficacy of the UB-CF instruction. Taking care to highlight the voices of marginalized users, we use the application as a prompt for surfacing potential harms perpetuated by FB ad recommendations, and qualitatively analyze themes of harm and proposed solutions provided by users themselves. The instruction increased comprehension of UB-CF for both groups, and we show that comprehension is associated with mentioning the mechanisms of the algorithm more in advocacy statements, a crucial component of a successful argument. We provide recommendations for increased algorithmic transparency on social media and for including marginalized voices in the conversation of algorithmic harm that are of interest both to social media researchers and ML educators.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2022 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

By which 'critical' means an intellectual stance of skepticism, centering the consequences, limitations, and unjust impacts of computing in society.

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.