Despite high approval ratings for unions and growing worker interest in organizing, employees in the United States still face significant barriers to securing collective bargaining agreements. A key factor is employer counter-organizing: efforts to suppress unionization through rule changes, retaliation, and disruption. Designing sociotechnical tools and strategies to resist these tactics requires a deeper understanding of the role computing technologies play in counter-organizing against unionization. In this paper, we examine three high-profile organizing efforts—at Amazon, Starbucks, and Boston University—using publicly available sources to identify four recurring technological tactics: surveillance, spacing, screaming and scabbing. We analyze how these tactics operate across contexts, highlighting their digital dimensions and strategic deployment. We conclude with implications for organizing in digitally-mediated workplaces, directions for future research, and emergent forms of worker resistance.

This study examines the pedagogical approaches and experiences of community-engaged educators—individuals who teach privacy, online safety, or security to specific communities through community organizations, companies, or local institutions, such as libraries. We draw on interviews with 21 such educators across the United States and find that, unlike some privacy and security advice that may emphasize knowledge retention of common skills and strategies, these educators prioritized teaching for independent decision-making. Our participants conceptualized privacy literacy as a process for taking informed action, and, from their insights, we identified five core competencies of privacy literacy: (1) data fluency, (2) account security, (3) fraud detection, (4) information vetting, and (5) surveillance capitalism. Notably, these competencies integrate privacy, security, and online safety concepts into privacy literacy—reflecting an increasingly integrated threat landscape. Embedded within the communities they serve, these educators shared their deep understanding of their students’ needs, which varied dramatically, and shared ways in which they tailored their programming accordingly. However, educators also shared significant teaching constraints, including limited time, resources, and organizational support. We discuss the implications of our findings for privacy literacy and for supporting community-engaged privacy literacy efforts.

In order to train, test, and evaluate nudity detection models, machine learning researchers typically rely on nude images scraped from the Internet. Our research finds that this content is collected and, in some cases, subsequently \emph{distributed} by researchers without consent, leading to potential misuse and exacerbating harm against the subjects depicted. \textbf{This position paper argues that the distribution of nonconsensually collected nude images by researchers perpetuates image-based sexual abuse and that the machine learning community should stop the nonconsensual use of nude images in research.} To characterize the scope and nature of this problem, we conducted a systematic review of papers published in computing venues that collect and use nude images. Our results paint a grim reality: norms around the usage of nude images are sparse, leading to a litany of problematic practices like distributing and publishing nude images with uncensored faces, and intentionally collecting and sharing abusive content. We conclude with a call-to-action for publishing venues and a vision for research in nudity detection that balances user agency with concrete research objectives.

YouTube has become an important part of the educational ecosystem, with millions of viewers seeking informative videos and help with coursework. Educational YouTubers create this content, often balancing pedagogical rigor and entertainment value. However, creators need not only to promote their content to find viewers, but also to monetize. In this study, we explore the tensions educational YouTubers face when making monetized educational content. We conduct a qualitative interview study with 12 popular educational YouTubers about their monetization strategies, perceptions of YouTube's algorithmic promotion of their content, and conception of their audience. We find that educational YouTubers are largely driven by a desire to share free and high-quality educational content, and that common monetization strategies like sponsorships and clickbait sometimes interfere with this mission. We describe the careful strategies our participants use to maintain educational integrity while making a living on an algorithmically-driven platform. We then use these findings to draw parallels between YouTubers' challenges with monetizing educational content and the history of educational public broadcast in the United States, which has followed a similar trajectory. In closing, we offer several recommendations for supporting educational YouTubers in creating the high-quality, publicly accessible educational content that is appreciated by a worldwide audience.

Sexting is a common and healthy behavior in romantic and sexual relationships. However, not every relationship lasts. When a relationship ends, the fate of sexual content that was previously shared can be a source of discomfort, anxiety, or fear for individuals who may no longer trust their former partners. In extreme cases, intimate content may be leaked or misused by its recipient. To investigate opportunities for building safer sexting tools with breakups in mind, we conducted a survey with 310 U.S. adults who have sexted in the last year. We asked about their sexting practices, communication practices within their relationship about sexting, and preferences for their own sexting content after a breakup. We find that most people save sexts in some form, either actively (e.g., via screenshots) or passively (e.g., in chat history). There is no consensus around what one should do with an ex’s content: although most (55%) want their content to be deleted at the end of a relationship, many others don’t care (25%) or even hope their ex keeps the material (11%). However, most have never spoken to their partner about this preference. We end with design recommendations that support sexting while keeping the entire relationship lifecycle in mind.

Systemic property dispossession from minority groups has often been carried out in the name of technological progress. In this paper, we identify evidence that the current paradigm of large language models (LLMs) likely continues this long history. Examining common LLM training datasets, we find that a disproportionate amount of content authored by Jewish Americans is used for training without their consent. The degree of over-representation ranges from around 2x to around 6.5x. Given that LLMs may substitute for the paid labor of those who produced their training data, they have the potential to cause even more substantial and disproportionate economic harm to Jewish Americans in the coming years. This paper focuses on Jewish Americans as a case study, but it is probable that other minority communities (e.g., Asian Americans, Hindu Americans) may be similarly affected and, most importantly, the results should likely be interpreted as a “canary in the coal mine” that highlights deep structural concerns about the current LLM paradigm whose harms could soon affect nearly everyone. We discuss the implications of these results for the policymakers thinking about how to regulate LLMs as well as for those in the AI field who are working to advance LLMs. Our findings stress the importance of working together towards alternative LLM paradigms that avoid both disparate impacts and widespread societal harms.

Non-consensual intimate media (NCIM) involves sharing intimate content without the depicted person's consent, including 'revenge porn' and sexually explicit deepfakes. While NCIM has received attention in legal, psychological, and communication fields over the past decade, it is not sufficiently addressed in computing scholarship. This paper addresses this gap by linking NCIM harms to the specific technological components that facilitate them. We introduce the sociotechnical stack , a conceptual framework designed to map the technical stack to its corresponding social impacts. The sociotechnical stack allows us to analyze sociotechnical problems like NCIM, and points toward opportunities for computing research. We propose a research roadmap for computing and social computing communities to deter NCIM perpetration and support victim-survivors through building and rebuilding technologies.

With over 1.5 million content creators, OnlyFans is one of the fastest growing subscription-based social media platforms. The platform is primarily associated with sexual content. Thus, OnlyFans creators are uniquely positioned at the intersection of professional social media content creation and sex work. While the motivations of experienced sex workers to adopt OnlyFans have been studied, in this work we seek to understand the motivations of creators who had not previously done sex work. Through a qualitative interview study of 22 U.S.-based OnlyFans creators, we find that beyond the typical motivations for pursuing gig work (e.g., flexibility, autonomy), our participants were motivated by three key factors: (1) societal visibility and mainstream acceptance of OnlyFans; (2) platform design and affordances such as boundary-setting with clients, privacy from the public, and content archives; and (3) the pandemic, as OnlyFans provided an enormous opportunity to overcome lockdown-related issues.

Trauma is the physical, emotional, or psychological harm caused by deeply distressing experiences. Research with communities that may experience high rates of trauma has shown that digital technologies can create or exacerbate traumatic experiences. Via three vignettes, we discuss how considering the possible effects of trauma and traumatic stress reactions provides an explanatory lens with new insights into people’s technology experiences. Then, we present a framework—trauma-informed computing—in which we adapt and show how to apply six key principles of trauma-informed approaches to computing: safety, trust, peer support, collaboration, enablement, and intersectionality. Through specific examples, we describe how to apply trauma-informed computing in four areas of computing research and practice: user experience research & design, security & privacy, artificial intelligence & machine learning, and organizational culture in tech companies. We conclude by discussing how adopting trauma-informed computing will lead to benefits for all users, not only those experiencing trauma.

How do people in a precarious profession leverage technology to grow their business and improve their quality of life? Sex workers sit at the intersection of multiple marginalized identities and make up a sizeable workforce: the United Nations estimates that at least 42 million sex workers are conducting business across the globe. Yet, little research has examined how well technology fulfills sex workers’ business needs in the face of unique social, political, legal, and safety constraints.

Phone numbers are intimately connected to our digital lives. People are increasingly required to disclose their phone number in digital spaces, both commercial and personal. While convenient for companies, the pervasive use of phone numbers as user identifiers also poses privacy, security, and access risks for individuals. In order to understand these risks, we present findings from a qualitative online elicitation study with 195 participants about their negative experiences with phone numbers, the consequences they faced, and how those consequences impacted their behavior. Our participants frequently reported experiencing phone number recycling, unwanted exposure, and temporary loss of access to a phone number. Resulting consequences they faced included harassment, account access problems, and privacy invasions. Based on our findings, we discuss service providers’ faulty assumptions in the use of phone numbers as user identifiers, problems arising from phone number recycling, and provide design and public policy recommendations for mitigating these issues with phone numbers.

Ballot marking devices (BMDs) allow voters to select candidates on a computer kiosk, which prints a paper ballot that the voter can review before inserting it into a scanner to be tabulated. Unlike paperless voting machines, BMDs provide voters an opportunity to verify an auditable physical record of their choices, and a growing number of U.S. jurisdictions are adopting them for all voters. However, the security of BMDs depends on how reliably voters notice and correct any adversarially induced errors on their printed ballots. In order to measure voters' error detection abilities, we conducted a large study (N = 241) in a realistic polling place setting using real voting machines that we modified to introduce an error into each printout. Without intervention, only 40% of participants reviewed their printed ballots at all, and only 6.6% told a poll worker something was wrong. We also find that carefully designed interventions can improve verification performance. Verbally instructing voters to review the printouts and providing a written slate of candidates for whom to vote both significantly increased review and reporting rates-although the improvements may not be large enough to provide strong security in close elections, especially when BMDs are used by all voters. Based on these findings, we make several evidence-based recommendations to help better defend BMD-based elections.

Undocumented immigrants in the United States face risks of discrimination, surveillance, and deportation. We investigate their technology use, risk perceptions, and protective strategies relating to their vulnerability. Through semi-structured interviews with Latinx undocumented immigrants, we find that while participants act to address offline threats, this vigilance does not translate to their online activities. Their technology use is shaped by needs and benefits rather than risk perceptions. While our participants are concerned about identity theft and privacy generally, and some raise concerns about online harassment, their understanding of government surveillance risks is vague and met with resignation. We identify tensions among self-expression, group privacy, and self-censorship related to their immigration status, as well as strong trust in service providers. Our findings have implications for digital literacy education, privacy and security interfaces, and technology design in general. Even minor design decisions can substantially affect exposure risks and well-being for such vulnerable communities.