Social media platforms are important venues for identity expression, and the Human-Computer Interaction community has been paying growing attention to how marginalized groups express their identities on these platforms. Joining the emerging literature on intersectional experiences, we study blind TikTokers (“BlindTokers”) who are also women and/or LGBTQ+. Using interview data from 41 participants, we identify their intersectional experiences as mediated by TikTok’s socio-technical affordances. We argue that BlindTokers’ intersectional marginalization is infrastructural: TikTok’s classification and moderation features interact with social norms in ways that push them aside and distort how they are treated on the platform. We use this infrastructure perspective to understand what these experiences are, how they were formed, and how they become harmful. We further recognize participants’ infrastructuring work to address these problems. This study guides future social media design with accessible creator tools, inclusive identity options, and context-aware moderation developed in partnership with communities.
This paper investigates how deeply entrenched offline gender norms and patriarchal power structures constrain and shape participation within Tech Aids, a large Facebook community in Pakistan focused on peer-to-peer technical help and e-commerce transactions. Through a qualitative study involving 14 months of participant observation and 20 semi-structured interviews (11 male, 9 female), we document significant gendered disparities: women exhibit lower public engagement, driven primarily by heightened privacy concerns, fear of harassment, and perceived male gatekeeping. Our analysis reveals that traditional socio-cultural restrictions on women's mobility and constrained interactions with unfamiliar men in physical tech marketplaces are directly mirrored in the online Tech Aids environment. To manage these risks, women actively engage in practices of digital purdah, utilizing workarounds like proxy-posting through male relatives to maintain both technical access and cultural modesty. By linking these offline barriers to online participation strategies, this study provides a vulnerability-centric framework and actionable design insights for creating more equitable online tech communities that explicitly address complex, deeply rooted socio-cultural constraints in non-WEIRD contexts.
LLMs becoming increasingly personalized to users’ language style raises both excitement and concerns for minority users such as Black American English (BAE) speakers. Yet, previous work has predominantly focused on user perceptions of out-of-context BAE statements by LLMs rather than naturalistic multi-turn interactions, and has ignored such systems’ effects on users’ self-perception. In this work, we examine the effects that multi-turn interactions with speech and text BAE-producing LLMs have on BAE speakers’ perceptions of the LLM and of themselves. We observe a significant change in participant self-esteem following the interactions, and notable qualitative differences between BAE-LLM and Standard American English (SAE) LLM interactions. We also observe significant effects of BAE-usage on user perception of the model within speech-based interactions. Our findings suggest that the effects of BAE-usage by an LLM agent on model- and self-perception among BAE-speaking users are complex and widely varied.
Women remain underrepresented in the workplace, partly due to stereotypes associating competence traits with men rather than women. Efforts to change such stereotypes often yield mixed results. As language models become integrated into daily life, AI writing assistants offer an opportunity to shift gender images. In a preregistered experiment (N=672), participants evaluated résumés for a female ("Jennifer") and a male ("John") candidate applying to a financial analyst role. They wrote evaluations using AI-generated suggestions in one of three conditions: suggestions for Jennifer integrated stereotypically male, female, or neutral traits. Suggestions for John remained neutral. Participants exposed to male-trait suggestions evaluated Jennifer as more competent, selected her as the leader, and offered higher salaries. However, we also observed signs of backlash: participants were less willing to work with competent Jennifer. We discuss implications for designing AI writing assistants to mitigate gender bias in hiring contexts.
AI-based systems such as language models have been shown to replicate and even amplify social biases reflected in their training data. Among other questionable behaviors, this can lead to AI-generated text--and text suggestions--that contain normatively inappropriate stereotypical associations. Little is known, however, about how this behavior impacts the writing produced by people using these systems. We address this gap by measuring how much impact stereotypes or anti-stereotypes in English single-word LM predictive text suggestions have on the stories that people write using those tools in a co-writing scenario. We find that (n=414), LM suggestions that challenge stereotypes sometimes lead to a significantly increased rate of anti-stereotypical co-written stories. However, despite this increased rate of anti-stereotypical stories, pro-stereotypical narratives still dominated the co-written stories, demonstrating that technical debiasing is only a partially effective strategy to alleviate harms from human-AI collaboration.
Archive of Our Own (AO3) is a prominent fanfiction platform widely recognized for its feminist design ethos, with a commitment to inclusive, pluralism and community-driven content creation. Among the content on it, Real Person Fiction (RPF) --- creations based on public figures rather than fictional characters --- offers a unique lens into how users engage with identity, visibility, and cultural narratives. In this study, we conduct a large-scale computational analysis to examine gender representation, thematic diversity, and occupational portrayals. Our findings reveal a significant gender imbalance, with man characters disproportionately over-represented. The readers themselves are also often portrayed as sexual figures. Overall, the relationship portrayals tend to mirror occupational roles, incorporate sexual elements, and reconstruct gender tropes. We interrogate how these patterns intersect with authorship, identity, and power. This work contributes to ongoing conversations about equity, ethics, and feminist values in digital content ecosystems and feminist HCI development.
This study investigates the role of large-team collaboration in shaping early-career scholars’ career development, with a focus on gender disparities. Using publication and collaboration data from SciSciNet in Computer Science, we capture the social capital accumulation process in academia with a neighborhood-based centrality metric and publication counts. Synthetic difference-in-differences (SDID) is applied to estimate the impact of early experience in large-team collaboration on subsequent research careers. Results indicate that junior scholars participating in large-team research significantly improve their network centrality, indicating more frequent collaborations with influential scholars, and produce approximately 0.75 more publications per year. Meanwhile, we document persistent gender gaps: men are 16\% more likely to access large-team collaborations. These findings highlight large-team collaboration as both a source of career acceleration and a mechanism of gender inequality. We conclude with implications for equity promotion and strategies enabling more inclusive collaboration.