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

Peer Reviewed

Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes "alignment" limits meaningful progress and cross-disciplinary collaboration. In this position paper, we argue that the research community should explicitly define and critically reflect on "alignment" to account for the bidirectional and dynamic relationship between humans and AI. Through a systematic review of over 400 papers spanning HCI, NLP, ML, and more, we examine how alignment is currently defined and operationalized. Building on this analysis, we introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI – supporting cognitive, behavioral, and societal adaptation to rapidly advancing AI technologies. Our findings reveal significant gaps in current literature, especially in long-term interaction design, human value modeling, and mutual understanding. We conclude with three central challenges and actionable recommendations to guide future research toward more nuanced, reciprocal, and human-AI alignment approaches.

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.

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.