Low-resource countries represent over 90% of maternal deaths, with Pakistan among the top four countries contributing nearly half in 2023. Since these deaths are mostly preventable, large language models (LLMs) can help address this crisis by automating health communication and risk assessment. However, sexual and reproductive health (SRH) communication in conservative contexts often relies on indirect language that obscures meaning, complicating LLM-based interventions. We conduct a two-stage study in Pakistan: (1) analyzing data from clinical observations, interviews, and focus groups with clinicians and patients, and (2) evaluating the interpretive capabilities of five popular LLMs on this data. Our analysis identifies two axes of communication (referential domain and expression approach) and shows LLMs struggle with semantic drift, myths, and polysemy in clinical interactions. We contribute: (1) empirical themes in SRH communication, (2) a categorization framework for indirect communication, (3) evaluation of LLM performance, and (4) design recommendations for culturally-situated SRH communication.

Menstrual health education (MHE) in Pakistan is constrained by cultural taboos and inadequate formal curricula, leaving women with few trusted resources to lean on. In response to these challenges, we introduce a WhatsApp-based chatbot powered by a large language model (LLM) and Retrieval-Augmented Generation (RAG), co-designed with Pakistani college women. Workshops (N=30) revealed key design requirements—support for Roman Urdu, use of subsidized platforms, and an expert-curated knowledge base. We then deployed the chatbot with 13 participants for two weeks (403 messages + interviews). Women used it to challenge cultural taboos, legitimize health concerns often dismissed as “normal”, and build reproductive health knowledge through iterative questioning. Yet, interactions also exposed tensions: reliance on cultural explanatory models, questions of trust and validation, and gendered persona of the chatbot itself. We contribute empirical insights, a stigma-aware design framework for culturally sensitive conversational AI, and a methodological lens foregrounding expert validation in intimate health domains.

Security and privacy (S&P) advice plays a crucial role in how people stay safe online. While prior work shows that the plethora of advice from varied sources makes it difficult for users to prioritize advice, the insights are primarily based on studies conducted in Western contexts. Other work shows that users outside the West have different S&P needs and thus, we cannot simply rely on advice curated in the West to generalize to the majority world - regions of Africa, Asia, Latin America, and the Middle East, where most of the world's population lives. We fill this gap by investigating S&P advice across nine majority world countries via 70 semi-structured interviews with local experts: cybercafe operators, tech repair specialists, and other community figures that people commonly rely on for tech support and S&P advice. We find that the advice provided by local experts in the majority world largely matches the advice they provide to their constituents and the advice from the West. However, we surface various significant barriers that hinder majority world users from implementing advice, including economic constraints, language barriers, and social friction from taking protective measures. Our findings further show how factors such as social norms and gender shape advice practices, e.g., by driving gendered advice-seeking. We discuss how S&P advice in the majority world can be improved and reflect on how the S&P community can better engage with local communities in conducting similar research.

We are again thrilled to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with an issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 211 papers, of which 192 were accepted from the January 2024 cycle, and 19 were accepted from the July 2024 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

We are again thrilled to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with an issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 313 papers, of which 45 were accepted from the January 2024 cycle, 85 were accepted from the July 2025 cycle, and 183 were accepted from the October 2025 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

We are extremely happy to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with this issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 209 papers, of which 175 were accepted from the Jan 2023 cycle, and 34 were accepted from the Jul 2023 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

We are delighted to present this issue of the Proceedings of the ACM on Human-Computer Interaction to the Computer-Supported Cooperative Work and Social Computing (CSCW) community. The issue contains 171 papers, of which 117 were accepted from the July 2023 cycle, and 55 were accepted from the January 2024 cycle, and one was withdrawn. Exploring a broad range of inspiring and timely topics, the issue collects the sustained efforts of a community of authors, external reviewers, Associate Chairs, and Editors. The result is an outstanding selection of high-quality contributions which are certain to shape the future of the field. Our deepest gratitude as Papers Chairs goes to this community for its determination to extend the CSCW traditions of collegiality and rigor in crafting extraordinary scholarship.

The understanding of how teenagers perceive, manage and perform privacy is less well-understood in spaces outside of Western, educated, industrialised, rich and democratic countries.

We explore privacy perceptions, beliefs and practices of low-literate, low-income users in Pakistan, a patriarchal and religious context with a literacy rate of approx. 68% and where 59% of mobile users have less than 6 years of formal education. Through a qualitative study with 40 participants (17 male and 23 female) we examine the cultural, religious, and familial structures that impact users perceptions, management, and control of their personal privacy. We reveal significant gendered differences in privacy understandings, privacy preserving practices and the access to privacy related knowledge. Our work also highlights the seminal impact religious beliefs have on men and women’s understandings and management of privacy and the prolific use of after-market modified apps to support users specific privacy needs. The privacy concerns raised by our participants provide HCI researchers with valuable insights into designing privacy affordances for vulnerable and diverse populations beyond Western, educated, industrialized, rich and democratic contexts.

This paper explores the creation, management and moderation of women-only online groups as digital safe spaces. We interview eleven founders and moderators of six distinct, closed, women-only Facebook groups that predominantly cater to women in and from the Global South. The study provides insights into the motivations and mechanisms for creating and moderating these safe spaces, the affordances of social networking sites that enable or hinder the creation and moderation of such spaces and finally, and the deep impact moderating such spaces has on the women who manage them. Based on these findings we discuss suggestions for specific technological affordances to enable and support digital safe spaces for marginalized and vulnerable communities.

We present findings from a three country study exploring the intersection between female intimate health and religious beliefs. Through a qualitative study with Muslim female populations in Pakistan, Bangladesh and Malaysia, three different Muslim majority contexts, we examine the deep impact Islamic beliefs have on female intimate health and well-being. Our study investigates the perceptions, attitudes and behaviours of Muslim women to their own intimate and sexual bodies through their experiences of menarche, marriage and reproduction and menopause. The intersection of religion and female sexual bodies and health is a neglected area within HCI and we highlight how inextricably specific Islamic values are linked with women’s reproductive health in Muslim communities. We further discuss the opportunities and challenges of designing technologies for religious, non-secular beliefs and values with the aim to improve intimate health practices amongst Muslim women and to broaden the scope of health design within HCI.

We examine the opportunities and challenges in designing for maternal health in low-income, low-resource communities in patriarchal and religious contexts. Pakistan faces a crisis in maternal health with a maternal mortality ratio of 178 deaths per 100,000 live births, as compared to the developed-country average of just 12 deaths per 100,000. Through a 6-month long qualitative, empirical study we examine the prevalent beliefs and practices around maternal health in Pakistan, the access women have to health-care, the existing religious practices that influence them and the agency they exert in their own health-care decision making. We reveal the rampant misinformation among mothers and health workers, house-hold power dynamics that impact maternal health and the deep link between maternal health and religious beliefs. We also show how current maternal health care interventions fit poorly into this context and discuss alternate design recommendations for meeting the maternal health needs of these women.

We explore the impact of the gender and accent of the voice recording in Interactive Voice Response (IVR) systems in low-literate and patriarchal contexts. We conducted a small randomized control trial (RCT) with 62 participants, prompting them to identify myths and factual statements from a list of 10 prompts. One of four sets of recordings were randomized for each participant: Male formal (MF), Male Informal (MI), Female Formal (FF), and Female Informal (FI). We found that (a) male participants found male voices as providing more accurate information, (b) formal voice made myths seem accurate to male participants, (c) there was a significant impact of participant education level on correctly identifying myths, and (d) female participants were more knowledgeable about maternal health facts. Our study provides some basic guidelines on the potential characteristics of the voice used in IVR systems when deployment is in low-literate, and patriarchal communities.

We explore peer support mechanisms in patriarchal contexts where a woman's movements, social relations and the seeking of support are restricted. We investigate the use of closed Facebook groups as a vital mechanism for access to anonymous peer support in Pakistan for taboo narratives like abortion, sexual harassment, rape, domestic abuse and issues relating to child-rearing and parenting. We discuss how technology is impacting these women's lives and allowing access to a safe, non-judgmental space to share their stories and collectively support other women within the same context. We analyze the posting and support seeking behaviours and the mechanisms for anonymity and privacy within a closed women only Facebook group of 15,000 members. Our study reveals the vital importance of such digital safe spaces and the mechanisms that allow them to remain judgment free and safe for support seeking in stigmatized contexts within vulnerable populations.

This paper explores the opportunities and challenges in designing peer-support mechanisms for low-income,low-literate women in Pakistan, a patriarchal and religious context where women's movements, social relationsand access to digital technologies are restricted. Through a qualitative, empirical study with 21 participantswe examine the cultural and patriarchal framework where shame and fear of defamation restrict the seekingof support for personal narratives around taboo subjects like abortion, sexual harassment, rape and domesticabuse. Based on our qualitative data we also conduct a second qualitative study using a technology probe with15 low-income, low-literate women to explore the specific design of peer-support technologies for supportseeking and the sharing of sensitive and taboo narratives in a deeply patriarchal society. The design concernsraised by our participants regarding privacy, anonymity and safety provide CSCW researchers with valuableguidelines about designing for social connections and support for vulnerable populations within a particularcontext.

This paper studies the use of Digital Financial Services (DFS) as a solution to women's financial inclusion in deeply patriarchal, resource constrained communities. Through a qualitative, empirical study we map the financial life cycles of 20 women micro-entrepreneurs in different cities in Pakistan and the challenges they face. We explore how technology is currently influencing these women's businesses and personal lives and reveal how mobile money is not tuned to the problems they face and their financial needs. We present alternate design directions for meeting the technological and financial needs of these women, circumnavigating the patriarchal structures that constrain them.

We investigate the feasibility of combining off-the-shelf virtual reality headsets and electroencephalography. EEG is a highly sensitive tool and subject to strong distortions when exerting physical force like mounting a VR headset on top of it that twists sensors and cables. Our study compares the signal quality of EEG in VR against immersive dome environments and traditional displays using an oddball paradigm experimental design. Furthermore, we compare the signal quality of EEG when combined with a commodity VR headset without modification against a modified version that reduces physical strain on the EEG headset. Our results indicate, that it is possible to combine EEG and VR even without modification under certain conditions. VR headset customisation improves signal quality results. Additionally, display latency of the different modalities is visible on a neurological level.

In a concurrent electroencephalography (EEG) and eye-tracking study, we explore the specific neural responses associated with saccadic eye movements. We hypothesise that there is a distinct saccade-related neural response that occurs well before a physical saccade and that this response is different for free, natural saccades versus forced saccades. Our results show a distinct and measurable brain response approximately 200 ms before a physical saccade actually occurs. This response is distinctly different for free saccades versus forced saccades. Our results open up possibilities of predicting saccades based on neural data. This is of particular relevance for creating effective gaze guidance mechanisms within a virtual reality (VR) environment and for creating faster brain computer interfaces (BCI).

There is a continuous effort by animation experts to create increasingly realistic and more human-like digital characters. However, as virtual characters become more human they risk evoking a sense of unease in their audience. This sensation, called the Uncanny Valley effect, is widely acknowledged both in the popular media and scientific research but empirical evidence for the hypothesis has remained inconsistent. In this paper, we investigate the neural responses to computer-generated faces in a cognitive neuroscience study. We record brain activity from participants (N = 40)} using electroencephalography (EEG) while they watch videos of real humans and computer-generated virtual characters. Our results show distinct differences in neural responses for highly realistic computer-generated faces such as Digital Emily compared with real humans. These differences are unique only to agents that are highly photorealistic, i.e. the `uncanny' response. Based on these specific neural correlates we train a support vector machine~(SVM) to measure the probability of an uncanny response for any given computer-generated character from EEG data. This allows the ordering of animated characters based on their level of `uncanniness'.

This study compares three popular modalities for analyzing perceived video quality; user ratings, eye tracking, and EEG. We contrast these three modalities for a given video sequence to determine if there is a gap between what humans consciously see and what we implicitly perceive . Participants are shown a video sequence with different artifacts appearing at specific distances in their field of vision; near foveal, middle peripheral, and far peripheral. Our results show distinct differences between what we saccade to (eye tracking), how we consciously rate video quality, and our neural responses (EEG data). Our findings indicate that the measurement of perceived quality depends on the specific modality used.

Image Based Rendering (IBR) allows interactive scene exploration from images alone. However, despite considerable development in the area, one of the main obstacles to better quality and more realistic visualizations is the occurrence of visually disagreeable artifacts. In this paper we present a methodology to map out the perception of IBR-typical artifacts. This work presents an alternative to traditional image and video quality evaluation methods by using an EEG device to determine the implicit visual processes in the human brain. Our work demonstrates the distinct differences in the perception of different types of visual artifacts and the implications of these differences.

In this article we use an ElectroEncephaloGraph (EEG) to explore the perception of artifacts that typically appear during rendering and determine the perceptual quality of a sequence of images. Although there is an emerging interest in using an EEG for image quality assessment, one of the main impediments to the use of an EEG is the very low Signal-to-Noise Ratio (SNR) which makes it exceedingly difficult to distinguish neural responses from noise. Traditionally, event-related potentials have been used for analysis of EEG data. However, they rely on averaging and so require a large number of participants and trials to get meaningful data. Also, due the the low SNR ERP's are not suited for single-trial classification. We propose a novel wavelet-based approach for evaluating EEG signals which allows us to predict the perceived image quality from only a single trial. Our wavelet-based algorithm is able to filter the EEG data and remove noise, eliminating the need for many participants or many trials. With this approach it is possible to use data from only 10 electrode channels for single-trial classification and predict the presence of an artifact with an accuracy of 85%. We also show that it is possible to differentiate and classify a trial based on the exact type of artifact viewed. Our work is particularly useful for understanding how the human visual system responds to different types of degradations in images and videos. An understanding of the perception of typical image-based rendering artifacts forms the basis for the optimization of rendering and masking algorithms.