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 socioeconomic 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.

Why is it so hard for AI chatbots to talk about race? By researching databases, natural language processing, and machine learning in conjunction with critical, intersectional theories, we investigate the technical and theoretical constructs underpinning the problem space of race and chatbots. We explore how the context of database corpora, the syntactic focus of language processing, and the unadjustable nature of deep learning algorithms cause bots to have difficulty handling race-talk. In each focus area, the tensions of this problem space open up possibilities for creating new technologies, theories, and relationships between people and machines. Through making tangible the abstract and disparate qualities involved in working with race and chatbots, we can pursue possible futures where chatbots are more capable of handling race-talk in its many forms. In this paper, we provide the HCI community with ways to tackle the question, how can chatbots handle racetalk 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.

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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.