In a world with shrinking queer spaces, dating apps serve as a useful way to find romantic or sexual partners, make friends, and develop connections in an often fragmented community. However, dating app structures may exclude queer users more than their cisgender heterosexual counterparts. In this paper, we examine how gender and sexuality are formatted as data in dating app profiles and algorithmically curated matchmaking functions. Based on an analysis of ten popular dating apps, we found gender and sexuality were often divided into descriptive labels and functional categories, which systematically marginalize or exclude queer identities from the matching process despite appearing inclusive. We present a framework for identity on dating apps, disentangling identity into a multi-layered construct of data schemas, profiles, algorithms, and platform ideology, thus enabling designers of data-driven systems to identify salient factors for users, even beyond queer identities and dating apps.

Large language models (LLMs) are the new hot trend being rapidly integrated into products and services—often, in chatbots. LLM-powered chatbots are expected to respond to any number of topics, including topics central to gender identity . In light of rising anti-trans discourse, we examined how two popular LLMs responded to real-world English-language questions about trans identity taken from Quora. We employed reflexive analysis that centered our situated knowledges of the trans community. We found that LLMs return pro-trans responses, even when presented with highly transphobic user prompts. While we also found highly transphobic LLM responses, we found that anti-trans sentiment in LLMs was often subtle, requiring a deep positional understanding from diverse trans stakeholders to interpret. Based on these findings, we recommend diverging from current “value-neutral” approaches that validate transphobia by taking an “all sides” approach. We provide considerations for both the evaluation and design of LLMs that center positional expertise.

Research into recidivism risk prediction in the criminal justice system has garnered significant attention from HCI, critical algorithm studies, and the emerging field of human-AI decision-making. This study focuses on algorithmic crime mapping, a prevalent yet underexplored form of algorithmic decision support (ADS) in this context. We conducted experiments and follow-up interviews with 60 participants, including community members, technical experts, and law enforcement agents (LEAs), to explore how lived experiences, technical knowledge, and domain expertise shape interactions with the ADS, impacting human-AI decision-making. Surprisingly, we found that domain experts (LEAs) often exhibited anchoring bias, readily accepting and engaging with the first crime map presented to them. Conversely, community members and technical experts were more inclined to engage with the tool, adjust controls, and generate different maps. Our findings highlight that all three stakeholders were able to provide critical feedback regarding AI design and use - community members questioned the core motivation of the tool, technical experts drew attention to the elastic nature of data science practice, and LEAs suggested redesign pathways such that the tool could complement their domain expertise.

Computer vision is a "data hungry" field. Researchers and practitioners who work on human-centric computer vision, like facial recognition, emphasize the necessity of vast amounts of data for more robust and accurate models. Humans are seen as a data resource which can be converted into datasets. The necessity of data has led to a proliferation of gathering data from easily available sources, including "public" data from the web. Yet the use of public data has significant ethical implications for the human subjects in datasets. We bridge academic conversations on the ethics of using publicly obtained data with concerns about privacy and agency associated with computer vision applications. Specifically, we examine how practices of dataset construction from public data-not only from websites, but also from public settings and public records-make it extremely difficult for human subjects to trace their images as they are collected, converted into datasets, distributed for use, and, in some cases, retracted. We discuss two interconnected barriers current data practices present to providing an ethics of traceability for human subjects: awareness and control. We conclude with key intervention points for enabling traceability for data subjects. We also offer suggestions for an improved ethics of traceability to enable both awareness and control for individual subjects in dataset curation practices.

The collection, organization, and retrieval of data about queer individuals and their identities challenge the creators and curators of highly structured database systems. While prior research in archival studies and demographics has examined processes of collecting and storing queer identities, they do not examine the complexities of highly democratized platforms that lack top-down mandates that often structure archival schemas. To examine the representation of queer people on open-platform databases, we performed a trace ethnography and thematic analysis of Wikidata, an open collaboration, highly structured database. We specifically examined the creation of, changes to, discussions around, and impacts of properties that encode queer identities, such assexual orientation andsex or gender. We found that changes often have unexpected impacts, that contributors struggled to determine vocabulary for queer identities which were accurate across the diverse cultural contexts of the Wikidata community, that the recording of queer identities could cause a stigmatizing effect for LGBTQ+ individuals, with further concerns of spreading rumors or outing closeted people, and that contributors proposing changes which would cause biased representations of queer people. Our analysis demonstrates inherent and unaddressed frictions when translating queer identities to the confines of a structured database. We conclude by discussing ways that the highly bottom-up, collaborative nature of platforms such as Wikidata, often seen as a major strength, can be vulnerable to individuals or small groups derailing and filibustering changes they disagree with on politically charged topics such as queer identities.

The study of eating disorders online has a long tradition within CSCW and HCI scholarship. Research within this body of work highlights the types of content people with eating disorders post as well as the ways in which individuals use online spaces for acceptance, connection, and support. However, despite nearly a decade of research, online eating disorder scholarship in CSCW and HCI rarely accounts for the ways gender shapes online engagement. In this paper, we present empirical results from interviews with 14 trans people with eating disorders. Our findings illustrate how working with gender as an analytic lens allowed us to produce new knowledge about the embodiment of participation in online eating disorder spaces. We show how trans people with eating disorders use online eating disorder content to inform and set goals for their bodies and how, as gender minorities within online eating disorder spaces, trans people occupy marginal positions that make them more susceptible to harms, such as threats to eating disorder validity and gender authenticity. In our discussion, we consider life transitions in the context of gender and eating disorders and address how online eating disorder spaces operate as social transition machinery. We also call attention to the labor associated with online participation as a gender minority within online eating disorder spaces, outlining several design recommendations for supporting the ways trans people with eating disorders use online spaces. CONTENT WARNING: This paper is about the online experiences of trans people with eating disorders. We discuss eating disorders, related content (e.g., thinspiration) and practices (e.g., binge eating, restriction), and gender dysphoria. Please read with caution.