The process of leaving high-pressure, identity-defining communities can produce profound identity changes. This leaving process propels some people to seek support online and to share their experiences publicly. We interviewed 13 social media content creators who made content as a part of, or in response to, their leaving process to understand their motivations and the ways audiences engaged with their work. We then explored how platforms transformed creators’ work into collaborative spaces for social support. As creators gained audiences, their visibility introduced new incentives, obligations, and risks. Creators had to manage the challenges of maintaining safe spaces for their audiences, meeting audience expectations, and addressing heightened safety concerns for themselves. We end by discussing the networked structure of creator-centered communities, the impacts of platform on creator communities, and the emotional harms associated with being at the center of a community focused on social support.

AI systems are testing lawyers' professional ethics obligations of competence, confidentiality, and candor. In the legal profession, the widespread availability of AI systems presents opportunities, like improving the review of documents during the discovery stage of a lawsuit, and challenges, illustrated by the handful of high-profile incidents where lawyers submitted legal briefs in court citing and describing fictitious cases based on AI-generated output. We conducted interviews with 44 legal professionals in the U.S. to understand how attorneys are making sense of AI technology and the impacts these technologies are having on their profession, legal ethics, and legal institutions. We describe participants' experiences with AI in legal work; opportunities and barriers for AI adoption; as well as beliefs, hopes, and concerns lawyers have about potential AI-induced social change. This work extends our understanding of AI's impact on knowledge work.

Two critical and interrelated questions regarding the design and study of programming languages are: 1) What does it mean to design a programming language? and 2) Why does minimal demographic diversity persist in the programming language community? In this paper, we present feminism as a philosophical lens for analyzing the programming languages field in order to help us understand and answer the motivating questions above. By using a feminist lens, we are able to explore how the dominant intellectual and cultural norms have both shaped and constrained programming languages. A key contribution of this analysis is the explanation of how marginalization in the programming language community limits the intellectual and demographic makeup of the field. We see this paper as an invitation to everyone in the programming languages field to deepen our collective understanding of the forces shaping our field. Our goal is to illustrate opportunities for more inclusive practices that will introduce greater diversity to the design of programming languages and the demographic makeup of the programming language community.

Centralized online social networks --- e.g., Facebook, Twitter and TikTok --- help drive social connection on the Internet, but have nigh unfettered access to monitor and monetize the personal data of their users. This centralization can especially undermine the use of the social internet by minority populations, who disproportionately bear the costs of institutional surveillance. We introduce a new class of privacy-enhancing technology --- decentralized privacy overlays (DePOs) --- that helps cOSN users regain some control over their personal data by allowing them to selectively share secret content on cOSNs through decentralized content distribution networks. As a first step, we present an implementation and user evaluation of Image DePO, a proof-of-concept design probe that allows users to upload and share secret photos on Facebook through the Interplanetary File System peer-to-peer protocol. We qualitatively evaluated Image DePO in a controlled, test environment with 19 queer and Black, Indigenous, (and) Person of Color (BIPOC) participants. We found that while Image DePO could help address the institutional threats with which our participants expressed concern, interpersonal threats were the more salient concern in their decisions to share content. Accordingly, we argue that in order to see widespread use, DePOs must align protection against abstract institutional threats with protection against the more salient interpersonal threats users consider when making specific sharing decisions.

Why is it so hard for chatbots to talk about race? This work explores how the biased contents of databases, the syntactic focus of natural language processing, and the opaque nature of deep learning algorithms cause chatbots difficulty in handling race-talk. In each of these areas, the tensions between race and chatbots create new opportunities for people and machines. By making the abstract and disparate qualities of this problem space tangible, we can develop chatbots that are more capable of handling race-talk in its many forms. Our goal is to provide the HCI community with ways to begin addressing the question, how can chatbots handle race-talk in new and improved ways?

Anonymity, ephemerality, and hyper-locality are an uncommon set of features in the design of online communities. However, these features were key to Yik Yak's initial success and popularity. In an interview-based study, we found that these three features deeply affected the identity of the community as a whole, the patterns of use, and the ways users committed to this community. We conducted interviews with 18 Yik Yak users on an urban American university campus and found that these three focal design features contributed to casual commitment, transitory use, and emergent community identity. We describe situated anonymity, which is the result of anonymity, ephemerality, and hyper-locality coexisting as focal design features of an online community. This work extends our understanding of use and identity-versus-bond based commitment, which has implications for the design and study of other atypical online communities.

Understanding users becomes increasingly complicated when we grapple with various overlapping attributes of an individual's identity. In this paper we introduce intersectionality as a framework for engaging with the complexity of users' "and authors" "identities", and situating these identities in relation to their contextual surroundings. We conducted a meta-review of identity representation in the CHI proceedings, collecting a corpus of 140 manuscripts on gender, ethnicity, race, class, and sexuality published between 1982-2016. Drawing on this corpus, we analyze how identity is constructed and represented in CHI research to examine intersectionality in a human-computer interaction (HCI) context. We find that previous identity-focused research tends to analyze one facet of identity at a time. Further, research on ethnicity and race lags behind research on gender and socio-economic class. We conclude this paper with recommendations for incorporating intersectionality in HCI research broadly, encouraging clear reporting of context and demographic information, inclusion of author disclosures, and deeper engagement with identity complexities.