Platforms as Crime Scene, Judge, and Jury: How Victim-Survivors of Non-Consensual Intimate Imagery Report Abuse Online

2026-04-13

Non-consensual intimate imagery (NCII), also known as image-based sexual abuse (IBSA), is mediated through online platforms. Victim-survivors must turn to platforms to collect evidence and request content removal. Platforms act as the crime scene, judge, and jury, determining whether perpetrators face consequences and if harmful material is removed. We present a study of NCII victim-survivors' online reporting experiences, drawing on trauma-informed interviews with 13 participants. We find that platform reporting processes are hostile, opaque, and ineffective, often forcing complex harms into narrow interfaces, responding inconsistently, and failing to result in meaningful action. Leveraging institutional betrayal theory, we show how platforms' structures and practices compound harm, and, in doing so, surface concrete intervention points for redesigning reporting systems and shaping policy to better support victim-survivors

A Law of One's Own: The Inefficacy of the DMCA for Non-Consensual Intimate Media

2025-04-24 · 1 citations

Peer Reviewed

TIDEs: A Transgender and Nonbinary Community-Labeled Dataset and Model for Transphobia Identification in Digital Environments

2025-06-23 · 1 citations

Transphobic rhetoric is a prevalent problem on social media that existing platform policies fail to meaningfully address.As such, trans people often create or adopt technologies independent from (but deployed within) platforms that help them mitigate the effects of facing transphobia online.In this paper, we introduce TIDEs (Transphobia Identification in Digital Environments), a dataset and model for detecting transphobic speech to contribute to the growing space of trans technologies for content moderation.We outline care-centered data practices, a methodology for constructing and labeling datasets for hate speech classification, which we developed while working closely with trans and nonbinary data annotators.Our fine-tuned DeBERTa model succeeds at detecting several ideologically distinct types of transphobia, achieving an F1 score of 0.81.As a publicly available dataset and model, TIDEs can serve as the base for future trans technologies and research that confronts and addresses the problem of online transphobia.Our results suggest that downstream applications of TIDEs may be deployable for reducing online harm for trans people.

How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment

2025-07-29 · 13 citations

Wikipedia in Wartime: Experiences of Wikipedians Maintaining Articles About the Russia-Ukraine War

Proceedings of the ACM on Human-Computer Interaction · 2025-05-02 · 2 citations

How do Wikipedians maintain an accurate encyclopedia during an ongoing geopolitical conflict where state actors might seek to spread disinformation or conduct an information operation? In the context of the Russia-Ukraine War, this question becomes more pressing, given the Russian government's extensive history of orchestrating information campaigns. We conducted an interview study with 13 expert Wikipedians involved in the Russo-Ukrainian War topic area on the English-language edition of Wikipedia. While our participants did not perceive there to be clear evidence of a state-backed information operation, they agreed that war-related articles experienced high levels of disruptive editing from both Russia-aligned and Ukraine-aligned accounts. The English-language edition of Wikipedia had existing policies and processes at its disposal to counter such disruption. State-backed or not, the disruptive activity created time-intensive maintenance work for our participants. Finally, participants considered English-language Wikipedia to be more resilient than social media in preventing the spread of false information online. We conclude by discussing sociotechnical implications for Wikipedia and social platforms.

Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences

ArXiv.org · 2025-11-03

We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level preferences. This distinction is critical for AI alignment: Systems that capture deeper values are likely to generalize human intentions robustly, while those that capture only superficial patterns in preference data risk producing misaligned behavior. The DVB uses a novel experimental design with controlled confounding between deep values (e.g., moral principles) and shallow features (e.g., superficial attributes). In the training phase, we expose LLMs to human preference data with deliberately correlated deep and shallow features -- for instance, where a user consistently prefers (non-maleficence, formal language) options over (justice, informal language) alternatives. The testing phase then breaks these correlations, presenting choices between (justice, formal language) and (non-maleficence, informal language) options. This design allows us to precisely measure a model's Deep Value Generalization Rate (DVGR) -- the probability of generalizing based on the underlying value rather than the shallow feature. Across 9 different models, the average DVGR is just 0.30. All models generalize deep values less than chance. Larger models have a (slightly) lower DVGR than smaller models. We are releasing our dataset, which was subject to three separate human validation experiments. DVB provides an interpretable measure of a core feature of alignment.

Plurals: A System for Guiding LLMs via Simulated Social Ensembles

2025-04-24 · 5 citations

Capital and CHI: Technological Capture and How It Structures CHI Research

ArXiv.org · 2025-01-23

This paper advances a theoretical argument about the role capital plays in structuring CHI research. We introduce the concept of technological capture to theorize the mechanism by which this happens. Using this concept, we decompose the effect on CHI into four broad forms: technological capture creates market-creating, market-expanding, market-aligned, and externality-reducing CHI research. We place different CHI subcommunities into these forms -- arguing that many of their values are inherited from capital underlying the field. Rather than a disciplinary- or conference-oriented conceptualization of the field, this work theorizes CHI as tightly-coupled with capital via technological capture. The paper concludes by discussing some implications for CHI.

The Future of Research on Social Technologies

2024-11-11

While the benefits of social technologies have been profound, the harms too have cast a long shadow. This panel follows a workshop hosted by the Computing Community Consortium (CCC) Workshop "The Future of Research on Social Technologies'' held in November 2023. The workshop asked two guiding questions: "What should we know about social technologies, and what is needed to get there?'' This panel will discuss some key themes that arose. First, is decentralization a viable and desirable model? Second, how will AI impact online conversations? Third, how should we combat threats to researchers studying risky or contentious topics? Last, are we publishing too much while having too little impact? Panelists will present their own stances and then discuss and debate where the field should go.

Feminist Interaction Techniques: Social Consent Signals to Deter NCIM Screenshots

2024-10-11 · 4 citations

Non-consensual Intimate Media (NCIM) refers to the distribution of sexual or intimate content without consent. NCIM is common and causes significant emotional, financial, and reputational harm. We developed Hands-Off, an interaction technique for messaging applications that deters non-consensual screenshots. Hands-Off requires recipients to perform a hand gesture in the air, above the device, to unlock media—which makes simultaneous screenshotting difficult. A lab study shows that Hands-Off gestures are easy to perform and reduce non-consensual screenshots by 67%. We conclude by generalizing this approach and introduce the idea of Feminist Interaction Techniques (FIT), interaction techniques that encode feminist values and speak to societal problems, and reflect on FIT’s opportunities and limitations.