CONTENT WARNING: This paper discusses image-based sexual abuse. Technological advances in Artificial Intelligence (AI) have made it easy to generate and distribute Non-Consensual Synthetic Intimate Imagery (NCSII); images and videos that depict people’s voices, faces, or bodies in intimate or sexually explicit scenarios. The creation and distribution of NCSII is a form of image-based sexual abuse that primarily affects marginalized people. We present Photo BOO-th, an interactive installation that invites attendees to encounter NCSII of themselves, resulting in a creepy, visceral, and affective experience. We designed Photo BOO-th to critique, raise awareness, and foster societal discussions around consent, image-based sexual abuse, and the role of technology in enabling harm. Following a Research through Design approach, we unpack design events that reveal some of the tensions and considerations in designing Photo BOO-th. We conclude with a discussion around designing creepy interactions in sensitive contexts, designing with and against the uncertainties in generative AI, and a reflection on vulnerability when designing creepy interactions.

Large Language Model-powered conversational agents (CAs) are increasingly capable of projecting sophisticated personalities through language, but how these projections affect users is unclear. We thus examine how CA personalities expressed linguistically affect user decisions and perceptions in the context of charitable giving. In a crowdsourced study, 360 participants interacted with one of eight CAs, each projecting a personality composed of three linguistic aspects: attitude (optimistic/pessimistic), authority (authoritative/submissive), and reasoning (emotional/rational). While the CA’s composite personality did not affect participants’ decisions, it did affect their perceptions and emotional responses. Particularly, participants interacting with pessimistic CAs felt lower emotional state and lower affinity towards the cause, perceived the CA as less trustworthy and less competent, and yet tended to donate more toward the charity. Perceptions of trust, competence, and situational empathy significantly predicted donation decisions. Our findings emphasize the risks CAs pose as instruments of manipulation, subtly influencing user perceptions and decisions.

Gender microaggressions are subtle yet persistent forms of discrimination in workplace interactions. While LLMs can detect them in written texts, it remains poorly understood how their interpretations align or diverge from human perspectives and experiences. We present a mixed-method study comparing how LLMs and humans differing in gender identity and lived experience, interpret gender microaggressions in the workplace. Using short dialogues adapted from real-world accounts, we asked 141 participants to rate the likelihood that a scenario contains a microaggression and provide a rationale for their answers. The same tasks were completed by 7 different LLM models. Our analysis reveals significant differences in how humans and LLMs interpret microaggressions, captured in both ratings and rationales, and more interestingly, the effect of gender and lived experience on human interpretations. These findings highlight the need for systems detecting microaggressions to embrace interpretive plurality, and support reflection and awareness while accounting for ambiguity.

As artificial intelligence (AI) becomes more embedded in personal health technology, its potential to transform health decision-making through personalised recommendations is becoming significant. However, there is limited understanding of how individuals perceive AI-assisted decision-making in the context of personal health. This study investigates the impact of AI-assisted decision-making on trust in physical activity-related health decisions. By employing MoveAI, a GPT-4.0-based physical activity decision-making tool, we conducted a mixed-methods study and conducted an online survey (N=184) and semi-structured interviews (N=24) to explore this dynamic. Our findings emphasise the role of nuanced personal health recommendations and individual decision-making styles in shaping trust in AI-assisted personal health decision-making. This paper contributes to the HCI literature by elucidating the relationship between decision-making styles and trust in the AI-assisted personal health decision-making process and showing the challenges of aligning AI recommendations with individual decision-making preferences.

Personal data flows across digital technologies integrated into people’s lives and relationships. Increasingly, these technologies include Generative AI. (How) should personal data flow into and out of GenAI models? We investigate how people experience personal data collection in GenAI ecosystems and unpack the enablers and barriers to governing their data. We focus on personal data collection by Meta, specifically Instagram, in line with their recent policy update on processing user data to train GenAI models. We conducted semi-structured interviews with 20 Latin American Instagram users, based in Europe and Latin America. We discussed the acceptability of their data flowing in and out of GenAI models through different scenarios. Our results interrogate power dynamics in data collection, the (inter)personal nature of data, and the multiple unknowns concerning data and their algorithmic derivatives. We pose provocations around feelings of powerlessness, reframing (inter)personal data, and encountering unknown data and algorithms through design.}, booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

This one-day studio aims to catalyze discussions and experimentation around non-textual academic documentation methods. With the understanding that human knowledge transcends written words, we aim to explore innovative ways to present and disseminate research outputs in diverse forms and of varying materiality. By bringing together researchers, practitioners, and academics from different disciplines and backgrounds, we seek to challenge the status quo of textual output and envision a future where knowledge production embraces the multisensory nature of human data.

Empathy is considered a crucial construct within HCI and CSCW, yet our understanding of this complex concept remains fragmented and lacks consensus in existing research. In this scoping review of 121 articles from the ACM Digital Library, we synthesize the diverse perspectives on empathy and scrutinize its current conceptualization and operationalization. In particular, we examine the various interpretations and definitions of empathy, its applications, and the methodologies, findings, and trends in the field. Our analysis reveals a lack of consensus on the definitions and theoretical underpinnings of empathy, with interpretations ranging from understanding the experiences of others to an affective response to the other's situation. We observed that despite the variety of methods used to gauge empathy, the predominant approach remains self-assessed instruments, highlighting the lack of novel and rigorously established and validated measures and methods to capture the multifaceted manifestations of empathy. Furthermore, our analysis shows that previous studies have used a variety of approaches to elicit empathy, such as experiential methods and situational awareness. These approaches have demonstrated that shared stressful experiences promote community support and relief, while situational awareness promotes empathy through increased helping behavior. Finally, we discuss a) the potential and drawbacks of leveraging empathy to shape interactions and guide design practices, b) the need to find a balance between the collective focus of empathy and the (existing and dominant) focus on the individual, and c) the careful testing of empathic designs and technologies with real-world applications.

Rising sustainability concerns in the food industry have driven the need for innovative approaches in culinary operations. Redesigning the menus and recipes from a sustainability perspective is a promising approach to reducing restaurants' environmental impact. Chefs, as crucial decision-makers in menu and recipe planning practices, play a vital role in promoting sustainable food services. However, the literature lacks insights into chefs' sustainable recipe planning practices and how information and communication technologies (ICTs) could support these practices. This paper addresses this gap by conducting individual interview sessions (n=10) and recipe generation workshops (n=10) with 20 chefs in total. It reveals four dimensions of sustainable recipes (locality, seasonality, frugality, and food quality) based on semi-structured interviews. It presents a novel interactive recipe planning concept called KNOBIE, which was designed to support chefs' sustainable recipe planning practices by using insights that gathered from the interviews. Lastly, based on an assessment of this concept through online recipe generation sessions with chefs, it provides five design implications for integrating ICTs into the sustainable menu and recipe planning practices to promote sustainable food services in restaurants.

The quality of social interaction has great importance for psychological and physiological health. Previous research indicates that smartphones have adverse effects on collocated social interactions. Most HCI works addressed this issue by restricting smartphone use during social interactions. Diverging from previous work, we designed WHISPER, an audio narrative box that aims to enrich collocated social interactions without restricting mobile technology use. We conducted a user study in a café environment with 21 participants to understand how users react to WHISPER and how it would influence their social interactions. In this paper, we present the result of this study and discuss four implications for technologies designed to enhance collocated social interactions (Respectfulness, Balanced Ambiguity, Adaptability, and Being Targeted) and two implications for research touching upon the HCI work on Design for Behavior Change and Collocated Interactions (Designing responsible interventions for accommodating unintended outcomes and Quantifying the quality of social interactions).

Reducing the environmental impact of current food production and consumption practices is a significant challenge for a more sustainable future. Even though previous HCI studies illustrated that design interventions could support more sustainable food practices in domestic contexts, little attention has been given to hospitality contexts (e.g., restaurants). Addressing this gap, we first investigated food preparation practices in restaurants (i.e., how recipes, menus and meals are prepared) through interviews with 10 chefs and instructors of culinary arts. Then, we designed KNOBIE, a design intervention aimed at supporting chefs’ sustainable food preparation practices through better recipe and menu planning. In this paper, we present the results of these interviews, KNOBIE as a concept to support sustainable recipe planning and how interviews guided its design.

Excessive smartphone use has negative effects on our social relations as well as on our mental and psychological health. Most of the previous work to avoid these negative effects is based on a top-down approach such as restricting or limiting users' use of smartphones. Diverging from previous work, we followed a bottom-up approach to understand the practice of smartphone use in public settings from the users' perspective. We conducted observations in four coffeehouses, six focus group sessions with 46 participants and three design workshops with 15 designers. We identified five themes that help better understand smartphone use behavior in public settings and four alternative design approaches to mediate this behavior, namely enlighteners, preventers, supporters, and compliers. We discuss the implications of these themes and approaches for designing future interactive technologies aimed at mediating excessive smartphone use behavior.