Identity facets such as gender, sexuality, and race shape consent processes, including in dating. Increasingly, dating apps play important roles in consent exchange processes. Examining consent in gendered and racialized communities, as mediated by dating apps, is an overlooked yet important space for illuminating the interplay between identity, technology, and consent. We draw from a guided reflective writing questionnaire (N=20) and semi-structured interviews (N=13) with self-identified second- and subsequent Arab and Southwest Asian and North African (SWANA) diaspora generations in the U.S. We investigate participants' online dating experiences with attention to consent-related values, behaviors, and experiences. Findings highlight the U.S. Arab/SWANA diaspora's technocultures of consent - a conceptual framework we use to describe the understandings and practices of consent that are influenced, co-produced, or expressed by the interaction between technology and people. We demonstrate how the technocultures of consent conceptual framework reveals connections between individuals' identities and social positions, consent-related beliefs and behaviors and technology design, norms, and expectations. We also introduce the concepts of networked consent and consent concept alignment tests, and offer design considerations to promote consent for all.

The overturning of Roe v. Wade in 2022 by the U.S. Supreme Court in Dobbs v. Jackson exposed and exacerbated existing gaps in reproductive privacy. In the post-Roe era, aggressive surveillance by both government and private entities has made real and heightened concerns about privacy violations for people capable of pregnancy (PCOP). We investigate PCOPs' reproductive privacy concerns and the strategies they use to address these concerns post-Roe. We conducted semi-structured interviews with 18 adult cisgender women and transgender men in the U.S. Our findings show that for PCOPs, privacy risks are both persistent and anomalous, imposing what we conceptualize as reproductive privacy labor , a type of safety and data work that is, to them, both necessary and exhausting. We introduce the conceptual framework Sociotechnical Reproductive Privacy , outlining the relationships between actors, technologies, and identities that are implicated in the many contexts of reproductive privacy vulnerabilities post-Roe. We conclude with considerations for research and design, and explore the utility of approaches like refusal and regulation (e.g., technical, policy) in promoting sociotechnical reproductive privacy. This research underscores the urgent need to address the intersection of reproductive rights, privacy, and technology, offering insights into how affected individuals navigate and manage their reproductive health decisions in an increasingly surveilled sociotechnical landscape.

Patent applications provide insight into how inventors imagine and legitimize uses of their imagined technologies; as part of this imagining they envision social worlds and produce sociotechnical imaginaries. Examining sociotechnical imaginaries is important for emerging technologies in high-stakes contexts such as the case of emotion AI to address mental health care. We analyzed emotion AI patent applications (N=58) filed in the U.S. concerned with monitoring and detecting emotions and/or mental health. We examined the described technologies' imagined uses and the problems they were positioned to address. We found that inventors justified emotion AI inventions as solutions to issues surrounding data accuracy, care provision and experience, patient-provider communication, emotion regulation, and preventing harms attributed to mental health causes. We then applied an ethical speculation lens to anticipate the potential implications of the promissory emotion AI-enabled futures described in patent applications. We argue that such a future is one filled with mental health conditions' (or 'non-expected' emotions') stigmatization, equating mental health with propensity for crime, and lack of data subjects' agency. By framing individuals with mental health conditions as unpredictable and not capable of exercising their own agency, emotion AI mental health patent applications propose solutions that intervene in this imagined future: intensive surveillance, an emphasis on individual responsibility over structural barriers, and decontextualized behavioral change interventions. Using ethical speculation, we articulate the consequences of these discourses, raising questions about the role of emotion AI as positive, inherent, or inevitable in health and care-related contexts. We discuss our findings' implications for patent review processes, and advocate for policy makers, researchers and technologists to refer to patent (applications) to access, evaluate and (re)consider potentially harmful sociotechnical imaginaries before they become our reality.

Emotion AI, or AI that claims to infer emotional states from various data sources, is increasingly deployed in myriad contexts, including mental healthcare. While emotion AI is celebrated for its potential to improve care and diagnosis, we know little about the perceptions of data subjects most directly impacted by its integration into mental healthcare. In this paper, we qualitatively analyzed U.S. adults' open-ended survey responses (n = 395) to examine their perceptions of emotion AI use in mental healthcare and its potential impacts on them as data subjects. We identify various perceived impacts of emotion AI use in mental healthcare concerning 1) mental healthcare provisions; 2) data subjects' voices; 3) monitoring data subjects for potential harm; and 4) involved parties' understandings and uses of mental health inferences. Participants' remarks highlight ways emotion AI could address existing challenges data subjects may face by 1) improving mental healthcare assessments, diagnoses, and treatments; 2) facilitating data subjects' mental health information disclosures; 3) identifying potential data subject self-harm or harm posed to others; and 4) increasing involved parties' understanding of mental health. However, participants also described their perceptions of potential negative impacts of emotion AI use on data subjects such as 1) increasing inaccurate and biased assessments, diagnoses, and treatments; 2) reducing or removing data subjects' voices and interactions with providers in mental healthcare processes; 3) inaccurately identifying potential data subject self-harm or harm posed to others with negative implications for wellbeing; and 4) involved parties misusing emotion AI inferences with consequences to (quality) mental healthcare access and data subjects' privacy. We discuss how our findings suggest that emotion AI use in mental healthcare is an insufficient techno-solution that may exacerbate various mental healthcare challenges with implications for potential distributive, procedural, and interactional injustices and potentially disparate impacts on marginalized groups.

Documenting weight-related journeys (e.g., weight loss, weight gain) is prevalent on social media, as is weight stigma, resulting in easily accessible personal archives filled with emotional, and potentially stigmatizing content. Through semi-structured interviews with 17 U.S.-based social media users sharing weight-related journeys, we investigate the motivations for and impacts of returning to previously posted weight-related social media content. We show how these personal archives foster a contested relationship between one's past and current self, where returning to past content facilitates dynamic interpretations of the self. We argue these interpretations' impacts cannot be understood without acknowledging and addressing the socio-technical context in which they exist: one filled with weight stigma, fatphobia, and narrow body ideals. We introduce the novel concepts of Transtemporal Support and Transtemporal Harm to describe the support and harms that one experiences in the present from returning to their past social media content. We posit that designs accounting for transtemporal support 1) can facilitate reflective sense-making for users who create repositories of digital artifacts about sensitive, potentially stigmatizing experiences on social media, and 2) should not perpetuate transtemporal harm.

Algorithms in online platforms interact with users' identities in different ways. However, little is known about how users understand the interplay between identity and algorithmic processes on these platforms, and if and how such understandings shape their behavior on these platforms in return. Through semi-structured interviews with 15 US-based TikTok users, we detail users' algorithmic folk theories of the For You Page algorithm in relation to two inter-connected identity types: person and social identity. Participants identified potential harms that can accompany algorithms' tailoring content to their person identities. Further, they believed the algorithm actively suppresses content related to marginalized social identities based on race and ethnicity, body size and physical appearance, ability status, class status, LGBTQ identity, and political and social justice group affiliation. We propose a new algorithmic folk theory of social feeds-The Identity Strainer Theory-to describe when users believe an algorithm filters out and suppresses certain social identities. In developing this theory, we introduce the concept of algorithmic privilege as held by users positioned to benefit from algorithms on the basis of their identities. We further propose the concept of algorithmic representational harm to refer to the harm users experience when they lack algorithmic privilege and are subjected to algorithmic symbolic annihilation. Additionally, we describe how participants changed their behaviors to shape their algorithmic identities to align with how they understood themselves, as well as to resist the suppression of marginalized social identities and lack of algorithmic privilege via individual actions, collective actions, and altering their performances. We theorize our findings to detail the ways the platform's algorithm and its users co-produce knowledge of identity on the platform. We argue the relationship between users' algorithmic folk theories and identity are consequential for social media platforms, as it impacts users' experiences, behaviors, sense of belonging, and perceived ability to be seen, heard, and feel valued by others as mediated through algorithmic systems.