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.

Anticipating the ethical and societal risks of emerging technologies has become an urgent challenge as their rapid integration into everyday life can produce far-reaching social consequences. In response, Design Futures practices are gaining traction within HCI and design as approaches to critically examine and anticipate the implications of technology. Yet, systematic knowledge on how these practices are structured to foster ethical reflection remains limited. To address this gap, we conducted a scoping review of 32 case studies employing Design Futures to engage with ethical concerns. Drawing from this review, we present a Taxonomy of Design Futures Processes for Ethical Reflection, which illustrates how different activities, actors' involvement, and types of futures generated shape the scope of ethical discussion. This taxonomy provides researchers and practitioners with practical guidance for creating Design Futures activities that foster ethical reflection on technology.

As values shape the design and governance of technology, it becomes critical to move beyond universal framings to explore the nuanced, subjective understandings individuals hold about values. Traditional value elicitation methods often identify values at play but overlook how they are interpreted through individuals’ social identities and lived experiences. This paper introduces an AI-augmented value exploration method inspired by photo elicitation, which involves interviews supported by participant-taken photographs. Instead, we use AI-generated imagery to uncover hidden associations and insights around personal understandings of values. In an exploratory study with six participants, we focused on the value of well-being, examining how AI-generated visuals prompted diverse personal interpretations and facilitated deeper value reflections. Our findings show that this method uncovers implicit meanings and deepens discussions by translating abstract ideas into tangible interpretations to yield richer data on situated values.

Informal caregivers’ engagement with patient data is becoming increasingly central to CSCW and HCI research on health management. Cardiac telerehabilitation (CTR) technologies generate lifestyle and well-being data that support patients and their families in recovery management, yet informal caregivers’ roles in CTR remain underexplored. Recreational athletes in rehabilitation are an especially under-researched group, despite their and their support system’s unique needs. Focusing on caregivers of recreational athletes, we conducted interviews with ten participants and used six visual scenarios of a dyadic CTR system to explore their perspectives on data and information co-participation. Caregivers reported that co-participation could strengthen dyadic coping and management but emphasized the need to balance important trade-offs. We provide design recommendations for dyadic CTR systems that balance care needs and preferences, promoting caregiver involvement in a supportive, non-supervisory role. We contribute to CSCW research by proposing a conceptual shift in technology-mediated rehabilitation care: positioning caregiver-inclusive CTR systems as negotiation tools that support boundary work and balance competing care values.

Cardiac telerehabilitation (CTR) relies heavily on telemonitoring, predominantly gathering automated biophysical or survey data for clinical decision-making. However, lifestyle change during and after cardiac rehabilitation (CR) outside hospitals is impacted by many contextual factors, including mental well-being or social support. Failure to acknowledge these factors in remote cardiac care could result in healthcare professionals (HCPs) offering standardized recommendations that hinder health management. To gain insights into the utilization of contextual information in clinical decision-making in Dutch CR, we conducted semi-structured interviews with seven HCPs and CR experts. Our data analysis highlights the importance of routine, physical, and psychosocial information during holistic clinical decision-making. We argue for a transition towards a holistic approach to telemonitoring in CTR, discussing implications for more inclusive and contextual data-gathering practices.

Background: Cardiac telerehabilitation (CTR) interventions can provide accessible and affordable remote rehabilitation services. However, as cardiac rehabilitation (CR) primarily targets inactive patients, little is known about the experiences with CR of highly active patients (ie, recreational athletes or, simply, athletes) with established coronary artery disease. Consequently, existing CTR interventions do not address the specific needs of the athletic subpopulation. Understanding the needs and values of athletes is crucial for designing meaningful CTR interventions that enhance user acceptance and engagement, thereby facilitating effective rehabilitation for this patient subgroup. Objective: This study aimed to inform the design of technologies that facilitate CTR for athletes. We intended to identify athletes' values related to CR, including health and sports tracking, as well as high-level requirements for technologies that can facilitate the CTR of athletes according to the identified values. Methods: We used value-sensitive design with a human-centric design approach to elicit design requirements for CTR that can serve athletes with established coronary artery disease. To identify athletes' values, we conducted 25 value-oriented semistructured interviews with 15 athletic patients and 10 health care professionals involved in CR programs. In a second phase, we conducted 6 card-sorting focus group sessions with 13 patients and 7 health care professionals to identify desired CTR features. Finally, we derived high-level CTR technology requirements connected to the athletes' needs and values. Results: We defined 12 athlete values divided into 3 categories: body centric, care centric, and data and technology centric. We clustered findings from the card-sorting activity into CTR technology requirements, such as remotely monitored sport-specific training and training data representations next to clinical limitations, and paired them with corresponding values. Conclusions: Athletes have distinct values and health goals in CR compared to general populations targeted by CTR interventions. Designing patient-centric CTR interventions that address these needs is crucial to support optimal recovery, safe return to sports, and adherence to CTR technologies in the home environment.

Social media platforms newsfeeds are generated by AI algorithms, which select and order posts based on user data. However, users are often unaware of what data is collected and employed for this aim, neither can they control it. To open up discussions on what data users are willing to feed the newsfeed algorithm with, we created the Facebook Data Shield, a human-size interactive installation where users can see and control what type of data is collected. By pressing buttons, data categories and/or data variables can be (de)activated. An outer rim with lights gives feedback to users about the level of personalization of the resulting newsfeed. We performed a preliminary study to get insights into what data users are willing to share, their preferred level of control, and the effect of such an installation on users' awareness. Based on our findings, we discuss implications for design and future work.

Ambiguity is gaining attention in self-tracking research as a means to go beyond the mere quantification of body signals. Recent research has suggested that ambiguity can be used even to enable social connection mediated by personal data. To explore this design space more widely, we organized two design workshops with a total of 67 participants. In this paper, we present three design concepts, as outcomes of the workshops, which use ambiguity to enable social self-tracking practices. We then discuss how these concepts demonstrate the potential of ambiguity to encourage collective sense-making, directly impact the user's social relationships, and offer multifaceted perspectives on reality.

Machine learning (ML) provides designers with a wide range of opportunities to innovate products and services. However, the design discipline struggles to integrate ML knowledge in education and prepare designers to ideate with ML. We propose the Mix and Match Machine Learning toolkit, which provides relevant ML knowledge in the form of tangible tokens and a web interface to support designers' ideation processes. The tokens represent data types and ML capabilities. By using the toolkit, designers can explore, understand, combine, and operationalize the capabilities of ML and understand its limitations, without depending on programming or computer science knowledge. We evaluated the toolkit in two workshops with design students, and we found that it supports both learning and ideation goals. We discuss the design implications and potential impact of a hybrid toolkit for ML on design education and practice.

Current solutions addressing misinformation on social media appear to rely on the misconception that misinformation is predominately spread by Artificial Intelligence (AI). However, the proliferation of false news is mainly due to humans. Solutions to curb misinformation should therefore emphasize human behavioral interventions, rather than focus solely on curbing AI bots. In this study, we analyze social media users' behaviors by means of a user journey. We create VisualBubble, a design probe that encourages reflection-oriented user experiences during news consumption on social media. We test our design probe with 10 users, to determine its effectiveness in increasing users' critical reflection on their news consumption behaviors. The initial findings show that VisualBubble can contribute to more critical attitudes towards the news that users are exposed to and, therefore, has the potential to mitigate social media misinformation.

When designing Machine Learning (ML) enabled solutions, designers often need to simulate ML behavior through the Wizard of Oz (WoZ) approach to test the user experience before the ML model is available. Although reproducing ML errors is essential for having a good representation, they are rarely considered. We introduce Wizard of Errors (WoE), a tool for conducting WoZ studies on ML-enabled solutions that allows simulating ML errors during user experience assessment. We explored how this system can be used to simulate the behavior of a computer vision model. We tested WoE with design students to determine the importance of considering ML errors in design, the relevance of using descriptive error types instead of confusion matrix, and the suitability of manual error control in WoZ studies. Our work identifies several challenges, which prevent realistic error representation by designers in such studies. We discuss the implications of these findings for design.