OBJECTIVE: To determine how much of the current biomedical literature would be flagged or require modification in relation to the presence of terms from leaked lists prepared by the Centers for Disease Control (CDC), the National Science Foundation (NSF), and the National Security Administration (NSA) in early 2025. MATERIALS AND METHODS: We searched PubMed (from 1996 to 2024) for all records that match at least one of the given terms, combined the terms and analyzed yearly and total frequency. RESULTS: At least 36.3% of all biomedical literature analyzed, representing more than 10 million records, would be flagged for review or modification with the given term lists. It is conservatively estimated that such term lists could impact more than 2.7 million biomedical publications over the next four years. DISCUSSION: Censorship of scientific findings and the use of term lists to judge the content of scientific materials could significantly impede scientific progress. CONCLUSION: Future research should investigate the long-term implications of, and interim strategies used to navigate, the imposition of censorship on the production and dissemination of scientific knowledge.

CSCW researchers have long inquired into the ways that identity informs, and is informed by, the design of technological systems. Gender is a regularly considered aspect of identity, with extensive work documenting and exploring gendered experiences and designs with the aim of addressing inequalities in, or through, design. Recent work has questioned the way that we conceptualise and "measure" gender, advocating more nuanced classificatory schemes to avoid silencing or obscuring trans and/or non-binary experiences. Building on and extending this research, our work examines how gender is conceptualised more broadly. Drawing from a range of theoretical perspectives in gender studies, feminist and postcolonial theory, we argue for the treatment of gender as "multiplicitous" when we conceptualise and interpret research, in order to avoid unintentionally perpetuating silencing and inequality even as we work to tackle it. Illustrating our argument with examples from both within and without CSCW, we suggest both new research directions for CSCW scholars inquiring into gender, and sensitising questions that scholars can use when constructing and evaluating studies.

Voice-Activated Artificial Intelligence (VAI) is increasingly ubiquitous, whether appearing as context-specific conversational assistants or more personalised and generalised personal assistants such as Alexa or Siri. CSCW and other fields have regularly studied the (positive and negative) social consequences of VAI design and deployment. One particular focus has been questions of gender, and the implications that the (often-feminine) gendering of VAIs has for societal norms and user experiences. Studies into this have largely elided transgender (trans) existences; the few exceptions to this operate largely from an external and predetermined idea of trans and/or non-binary user needs, centered on representation. In this study, we undertook a series of qualitative interviews with trans and/or non-binary users of VAIs to explore their experiences and needs. Our results show that these needs are far more than simply improvements to representation, and that users raise substantial concerns around the underlying framing of gender by even well-intentioned developers, the privacy and safety implications of ubiquitous VAI, and the motivations of the vast for-profit companies that deploy much of this technology. We provide both immediate recommendations for designers and researchers seeking to create trans-inclusive VAIs, and wider, critical proposals for how we as researchers go about assessing technological systems and appropriate points of intervention.

An ever-increasing body of work within HCI investigates questions of around “Women’s Health” with the aim to disrupt the status quo of defaulting to an implicit norm of cis-male bodies. This laudable and feminist project has the potential to drastically improve the inclusivity and availability of health care. To explore how this research attends to gender, embodiment and identity, we conducted a critical discourse analysis of 17 publications explicitly positioning themselves as works concerned with “Women’s Health”. We find essentialised articulations of embodiment and gender, though little discussion on the intersections of race, class, sexuality and cultural contexts. Through two speculative designs, we illustrate potential responses to our analysis: The Shadow Zine, a reflection of self and the Compass, a token for community care. 1 Our work provides an opportunity to develop a broader frame of gender and health, one that centers (gendered) marginalised health by attending to the power structures of existing medical practices and norms.

The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy-an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower-systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.

Autistic children are increasingly a focus of technology research within the Human-Computer Interaction (HCI) community. We provide a critical review of the purposes of these technologies and how they discursively conceptualise the agency of autistic children. Through our analysis, we establish six categories of these purposes: behaviour analysis, assistive technologies, education, social skills, therapy and well-being. Further, our discussion of these purposes shows how the technologies embody normative expectations of a neurotypical society, which predominantly views autism as a medical deficit in need of ‘correction’. Autistic children—purportedly the beneficiaries of these technologies—thus become a secondary audience to the largely externally defined purposes. We identify a lack of design for technologies that are geared towards the interests, needs and desires of autistic children. To move HCI’s research into autism beyond this, we provide guidance on how to consider agency in use and explicitly allow for appropriation beyond externally driven goals.

Automatic Gender Recognition (AGR) is a subfield of facial recognition that aims to algorithmically identify the gender of individuals from photographs or videos. In wider society the technology has proposed applications in physical access control, data analytics and advertising. Within academia, it is already used in the field of Human-Computer Interaction (HCI) to analyse social media usage. Given the long-running critiques of HCI for failing to consider and include transgender (trans) perspectives in research, and the potential implications of AGR for trans people if deployed, I sought to understand how AGR and HCI understand the term "gender", and how HCI describes and deploys gender recognition technology. Using a content analysis of papers from both fields, I show that AGR consistently operationalises gender in a trans-exclusive way, and consequently carries disproportionate risk for trans people subject to it. In addition, I use the dearth of discussion of this in HCI papers that apply AGR to discuss how HCI operationalises gender, and the implications that this has for the field's research. I conclude with recommendations for alternatives to AGR, and some ideas for how HCI can work towards a more effective and trans-inclusive treatment of gender.

Donation-based support for open, peer production projects such as Wikipedia is an important mechanism for preserving their integrity and independence. For this reason understanding donation behavior and incentives is crucial in this context. In this work, using a dataset of aggregated donation information from Wikimedia's 2015 fund-raising campaign, representing nearly 1 million pages from English and French language versions of Wikipedia, we explore the relationship between the properties of contents of a page and the number of donations on this page. Our results suggest the existence of a reciprocity mechanism, meaning that articles that provide more utility value attract a higher rate of donation. We discuss these and other findings focusing on the impact they may have on the design of banner-based fundraising campaigns. Our findings shed more light on the mechanisms that lead people to donate to Wikipedia and the relation between properties of contents and donations.