Values Across Contexts: Understanding How Older Adults Enact What Matters Through TechnologyAs populations age and technology becomes more pervasive, understanding the alignment between older adults' values and technology design is paramount. More research is needed to understand how older adults’ living contexts shape their values and the use of technology. To address this, through a multi-context study, we explored how values differ for older adults and how their context of living might influence the adoption and use of technology. We conducted 22 semi-structured interviews with older adults in various residential contexts. We show that older adults tend to prioritize the same core values across living contexts, yet how they express values in each context differs. Technology can amplify or inhibit key values. We describe implications for context-responsive technology and design for continuity, to allow older adults to continually uphold important values through technology use.2026HSHugo Simão et al.Universidade de LisboaAging-Friendly Technology DesignAging-in-Place Assistance SystemsCHI
Sometimes You Need Facts, and Sometimes a Hug: Understanding Older Adults’ Preferences for Explanations in LLM-Based Conversational AI SystemsDesigning Conversational AI systems to support older adults requires these systems to explain their behavior in ways that align with older adults’ preferences and context. While prior work has emphasized the importance of AI explainability in building user trust, relatively little is known about older adults’ requirements and perceptions of AI-generated explanations. To address this gap, we conducted an exploratory Speed Dating study with 23 older adults to understand their responses to contextually grounded AI explanations. Our findings reveal the highly context-dependent nature of explanations, shaped by conversational cues such as the content, tone, and framing of explanation. We also found that explanations are often interpreted as interactive, multi-turn conversational exchanges with the AI, and can be helpful in calibrating urgency, guiding actionability, and providing insights into older adults’ daily lives for their family members. We conclude by discussing implications for designing context-sensitive and personalized explanations in Conversational AI systems.2026NMNiharika Mathur et al.Georgia Institute of TechnologyHuman-LLM CollaborationExplainable AI (XAI)Aging-Friendly Technology DesignCHI
Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry PractitionersTheory of Mind (ToM)—the ability to infer transient mental states—is traditionally considered fundamental to human social interactions. This has sparked growing efforts in building and benchmarking AI’s ToM, yet little is known about how it could translate into the design and experience of everyday user-facing AI products and services. We conducted 13 co-design sessions with 26 U.S.-based AI practitioners to envision, reflect, and distill design recommendations for ToM-enabled everyday AI systems that are both future-looking and grounded in the realities of AI design and development practices. Analysis revealed three interrelated design recommendations: ToM-enabled AI should 1) be situated in the social context that shape users' mental states, 2) be responsive to the dynamic nature of mental states, and 3) be attuned to subjective individual differences. We surface design tensions within each recommendation that reveal a broader gap between practitioners' envisioned futures of ToM-enabled AI and the realities of current AI development practices. These findings point toward the need to move beyond static, inference-driven approach to ToM and toward designing ToM as a pervasive capability that supports continuous human-AI interaction loops.2026QWQiaosi Wang et al.Carnegie Mellon UniversityBrain-Computer Interface (BCI) & NeurofeedbackHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Toolset Action Coding System: A Generalized Method for Tool MeasurementThe evolution of artist toolsets has created opportunities for new media and industrial growth. As these tools evolve, clear advantages of digital media emerge. Still, many digital toolsets grow further apart from their traditional predecessors, developing highly technical workflows and unfamiliar interfaces. With no standard language or classification system across artist toolsets, the measurement of these various paradigms becomes convoluted. Moreover, it is unclear how augmenting production pipelines affects the practitioner workflow within a professional studio. This paper presents a generalized approach to measuring artist tools through data collection, field studies, interviews, and an iterative refinement process. We define a broad category of creative practice known as the ‘sculptural process’ and introduce a taxonomy for a technical coding system. Our findings indicate an obstruction of artist processes for professionals in digital media, particularly 3D, exposing a significant gap between traditional and digital media. These measurements create a foundation to reinforce artist-driven design in human-computer interaction.2026MMMoshe Mahler et al.Carnegie Mellon University3D Modeling & AnimationPrototyping & User TestingComputational Methods in HCICHI
Unremarkable to Remarkable AI Agent: Exploring Boundaries of Agent Intervention for Adults With and Without Cognitive ImpairmentAs the population of older adults increases, there is a growing need for support for them to age in place. This is exacerbated by the growing number of individuals struggling with cognitive decline and shrinking number of youth who provide care for them. Artificially intelligent agents could provide cognitive support to older adults experiencing memory problems, and they could help informal caregivers with coordination tasks. To better understand this possible future, we conducted a speed dating with storyboards study to reveal invisible social boundaries that might keep older adults and their caregivers from accepting and using agents. We found that healthy older adults worry that accepting agents into their homes might increase their chances of developing dementia. At the same time, they want immediate access to agents that know them well if they should experience cognitive decline. Older adults in the early stages of cognitive decline expressed desire for agents that can ease the burden they saw themselves becoming for their caregivers. They also speculated that an agent who really knew them well might be an effective advocate for their needs when they were less able to advocate for themselves. That is, the agent may need to transition from being unremarkable to remarkable. Based on these findings, we present design opportunities and considerations for agents and articulate directions of future research.2025MCMai Lee Chang et al.Humanized AI: Avatars, Agents, and Voice AssistantsCSCW
Data Wagers in Worker Advocacy ResearchThis paper draws on Michel de Certeau’s notion of "tactics" to explore the use of data in labor organizing research in CSCW. Taking a historical view, we first analyze a set of cases from 20th century US labor history that offer three distinct lenses on the risks of data-based advocacy campaigns: wagers, compromises, and concessions. Across our cases, we frame reformers' use of data tactics as a rhetorical move, taken to advance incremental worker gains under conditions of precarity. However, by continuing to rely on certain data-based arguments in the short term, we argue that labor reformers may have limited the frame of debate for broader arguments necessary to improve conditions in the long-term. These tensions follow us into data-based advocacy research in the present, such as the emerging "digital workerism" movement. To ensure the continuation of responsible advocacy research in CSCW, we offer insights from social justice movements to suggest how members of the HCI and CSCW communities can work more intentionally alongside (or without) data methods to support worker-led direct action.2025FSFranchesca Spektor et al.Advocacy WorkCSCW
Working Together: Algorithmic Management and Peer Relationships in the Hospitality IndustryAlgorithmic management is transforming traditional face-to-face service sectors like hospitality. To understand this phenomenon, we conducted an interview study in a unionized, mid-sized urban hotel on the West Coast of the USA. Through this work, we examine how an algorithmic management (AM) platform mediates work in a housekeeping department. Our analysis highlights the effects of AM on social processes, revealing that despite careful configuration, the tool's implementation still challenges traditional communication and coordination. This study contributes empirical evidence on AM impacts in a collaborative service environment, emphasizing the importance of organizational dynamics in AM design and implementation. We offer design opportunities for flexible workplace technologies that support, rather than frustrate, the relational aspects of service work.2025FSFranchesca Spektor et al.Social Platform Design & User BehaviorImpact of Automation on WorkDIS
Exploring the Innovation Opportunities for Pre-trained ModelsInnovators transform the world by understanding where services are successfully meeting customers’ needs and then using this knowledge to identify failsafe opportunities for innovation. Pre-trained models have changed the AI innovation landscape, making it faster and easier to create new AI products and services. Understanding where pre-trained models are successful is critical for supporting AI innovation. Unfortunately, the hype cycle surrounding pre-trained models makes it hard to know where AI can really be successful. To address this, we investigated pre-trained model applications developed by HCI researchers as a proxy for commercially successful applications. The research applications demonstrate technical capabilities, address real user needs, and avoid ethical challenges. Using an artifact analysis approach, we categorized capabilities, opportunity domains, data types, and emerging interaction design patterns, uncovering some of the opportunity space for innovation with pre-trained models.2025MPMinjung Park et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationExplainable AI (XAI)DIS
Designing Aging Reflection Probes to Elicit Self-Perception of Aging (SPA) Beliefs of Older Adults in IndiaAge-related transitions can influence older adults’ internalized aging beliefs, or Self-Perception of Aging (SPA). Previous studies have shown correlations between SPA and the well-being of older adults. However, there is a lack of specific tools to gain an in-depth understanding of SPA beliefs. This pictorial provides a detailed description of a probe designed to collect SPA-related insights directly from older adults. We describe the iterative co-design process of the 7-day Aging Reflection probe kit, incorporating feedback from pilot and focus group sessions with participants to refine the final design. We also highlight the design decisions made for the cultural adaptation of the probes to ensure they resonate with Indian participants. Our probe kit was instrumental in creating dialogue with participants about various aspects of SPA. Participants used the probes to refresh their memory during follow-up interviews. Insights from the probes played a critical role in conducting semi-structured interviews, advancing our understanding of how to operationalize SPA in HCI research and design.2025NKNeeta M Khanuja et al.Aging-Friendly Technology DesignParticipatory DesignDIS
Making the Right Thing: Bridging HCI and Responsible AI in Early-Stage AI Concept SelectionAI projects often fail due to financial, technical, ethical, or user acceptance challenges—failures frequently rooted in early-stage decisions. While HCI and Responsible AI (RAI) research emphasize this, practical approaches for identifying promising concepts early remain limited. Drawing on Research through Design, this paper investigates how early-stage AI concept sorting in commercial settings can reflect RAI principles. Through three design experiments—including a probe study with industry practitioners—we explored methods for evaluating risks and benefits using multidisciplinary collaboration. Participants demonstrated strong receptivity to addressing RAI concerns early in the process and effectively identified low-risk, high-benefit AI concepts. Our findings highlight the potential of a design-led approach to embed ethical and service design thinking at the front end of AI innovation. By examining how practitioners reason about AI concepts, our study invites HCI and RAI communities to see early-stage innovation as a critical space for engaging ethical and commercial considerations together.2025JJJi-Youn Jung et al.AI Ethics, Fairness & AccountabilityParticipatory DesignSustainable HCIDIS
AI Mismatches: Identifying Potential Algorithmic Harms Before AI DevelopmentAI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system’s actual performance falls short of what is needed to ensure safety and co-create value. These mismatches are particularly difficult to address once development is underway, highlighting the need for early-stage intervention. Navigating complex, multi-dimensional risk factors that contribute to AI Mismatches is a persistent challenge. To address it, we propose an AI Mismatch approach to anticipate and mitigate risks early on, focusing on the gap between realistic model performance and required task performance. Through an analysis of 774 AI cases, we extracted a set of critical factors, which informed the development of seven matrices that map the relationships between these factors and highlight high-risk areas. Through case studies, we demonstrate how our approach can help reduce risks in AI development.2025DSDevansh Saxena et al.University of Wisconsin-Madison, The Information SchoolAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasCHI
Dynamic Agent Affiliation: Who Should the AI Agent Work for in the Older Adult's Care Network?The population of older adults experiencing cognitive decline is growing faster than the number of workers who can care for them. Artificially intelligent (AI) agents could assist these older adults, keeping them in their homes longer. For this to happen, older adults must be willing to adopt and rely on agents. Would they trust an agent that might need to report their decline to others? We conducted a speed dating study exploring the impact of agent affiliation (i.e., who the agent should work for). Our healthy and declining participants reacted positively to the idea of agents supporting them. They particularly recognized how the agent would reduce the burden placed on their family caregivers. They viewed affiliation to be dynamic, shifting from the declining older adult and orienting more to their caregivers over the course of cognitive decline. They envisioned the agent modifying its decision-making process to be like their caregivers'.2024MCMai Lee Chang et al.Elderly Care & Dementia SupportAging-in-Place Assistance SystemsHuman-Robot Collaboration (HRC)DIS
Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy RisksPrivacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by analyzing 321 documented AI privacy incidents. We codified how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, differential privacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI.2024HLHao-Ping (Hank) Lee et al.Carnegie Mellon UniversityAI Ethics, Fairness & AccountabilityPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Charting the Automation of Hospitality: An interdisciplinary literature review examining the evolution of frontline service work in the face of algorithmic managementRecent investments in automation and AI are reshaping the hospitality sector. Driven by social and economic forces affecting service delivery and an impulse to seek out efficiencies, these new technologies have transformed the labor that acts as the backbone to the industry—namely frontline service work performed by housekeepers, front desk staff, line cooks and others. We describe the context for recent technological adoption, with particular emphasis on algorithmic management applications. Through this work, we identify gaps in existing literature and highlight areas in need of further research in the domains of worker-centered technology development. Our analysis highlights how technologies such as algorithmic management shape roles and tasks in the high-touch service sector. We outline how harms produced through automation are often due to a lack of attention to non-management stakeholders. We describe an opportunity space for researchers and practitioners to elicit worker participation at all stages of technology adoption, and offer methods for centering workers, increasing transparency, and accounting for the context of use through holistic implementation and training strategies.2023FSFranchesca Spektor et al.Platform Mediated EconomiesCSCW
Designing for Wellbeing: Worker-Generated Ideas on Adapting Algorithmic Management in the Hospitality IndustryLabor shortages have shaped many industries over the past several years, with hospitality experiencing one of the largest rates of attrition. Workers are leaving their jobs for a variety of reasons, ranging from burnout and work intensification to a lack of meaningful employment. While some literature maintains that labor-replacing automation is poised to bridge the shortages, we argue there is an opportunity for technology design to instead improve job quality and retention. Drawing on interviews with unionized guest room attendants, we report on workers’ perceptions of a widely-used algorithmic room assignment system. We then present worker-generated design ideas that adapt this system toward supporting three key facets of wellbeing: self-efficacy, transparency, and workload. We argue for the need to consider these facets of wellbeing through design across the service landscape, particularly as HCI attends to the impacts of AI and automation on frontline work.2023FSFranchesca Spektor et al.Workplace Wellbeing & Work StressImpact of Automation on WorkDIS
Creating Design Resources to Scaffold the Ideation of AI ConceptsAdvances in artificial intelligence have enabled unprecedented technical capabilities, yet making these advances useful in the real world remains challenging. We engaged in a Research through Design process to improve the ideation of AI products and services. We developed a design resource capturing AI capabilities based on 40 AI features commonly used across various domains. To probe its usefulness, we created a set of slides illustrating AI capabilities and asked designers to ideate AI-enabled user experiences. We also incorporated capabilities into our own design process to brainstorm concepts with domain experts and data scientists. Our research revealed that designers should focus on innovations where moderate AI performance creates value. We reflect on our process and discuss research implications for creating and assessing resources to systematically explore AI’s problem-solution space.2023NYNur Yildirim et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationPrototyping & User TestingDIS
How Experienced Designers of Enterprise Applications Engage AI as a Design MaterialHCI research has explored AI as a design material, suggesting that designers can envision AI's design opportunities to improve UX. Recent research claimed that enterprise applications offer an opportunity for AI innovation at the user experience level. We conducted design workshops to explore the practices of experienced designers who work on cross-functional AI teams in the enterprise. We discussed how designers successfully work with and struggle with AI. Our findings revealed that designers can innovate at the system and service levels. We also discovered that making a case for an AI feature's return on investment is a barrier for designers when they propose AI concepts and ideas. Our discussions produced novel insights on designers' role on AI teams, and the boundary objects they used for collaborating with data scientists. We discuss the implications of these findings as opportunities for future research aiming to empower designers in working with data and AI.2022NYNur Yildirim et al.Carnegie Mellon UniversityGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationCHI
Social Robots in Service Contexts: Exploring the Rewards and Risks of Personalization and Re-embodimentSocial agents and robots are moving into front-line positions in brick and mortar services, taking on roles where they directly interact with customers. These agents could potentially recognize customers to personalize service. Will customers like this, or might they feel monitored and profiled? Robots could also re-embody (move their "personality" between one body and another) in order to take on multiple roles that are typically performed by different people. Will this make customers feel more taken care of, or will it raise concerns about the robot’s competence and expertise? Our work investigates when robots should and should not recognize customers and re-embody. Our online study used storyboards to present possible future interactions between robots and customers across several different service contexts. Our findings suggest that people generally accept robots identifying customers and taking on vastly different roles. However, in some contexts, these robot behaviors seem creepy and untrustworthy2021SRSamantha Reig et al.Agent Personality & AnthropomorphismSocial Robot InteractionDIS
Wikipedia ORES Explorer: Visualizing Trade-offs For Designing Applications With Machine Learning APIWith the growing industry applications of Artificial Intelligence (AI) systems, pre-trained models and APIs have emerged and greatly lowered the barrier of building AI-powered products. However, novice AI application designers often struggle to recognize the inherent algorithmic trade-offs and evaluate model fairness before making informed design decisions. In this study, we examined the Objective Revision Evaluation System (ORES), a machine learning (ML) API in Wikipedia used by the community to build anti-vandalism tools. We designed an interactive visualization system to communicate model threshold trade-offs and fairness in ORES. We evaluated our system by conducting 10 in-depth interviews with potential ORES application designers. We found that our system helped application designers who have limited ML backgrounds learn about in-context ML knowledge, recognize inherent value trade-offs, and make design decisions that aligned with their goals. By demonstrating our system in a real-world domain, this paper presents a novel visualization approach to facilitate greater accessibility and human agency in AI application design.2021ZYZining Ye et al.Explainable AI (XAI)Interactive Data VisualizationDIS
Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple ObjectivesArtificial intelligence algorithms have been used to enhance a wide variety of products and services, including assisting human decision making in high-stake contexts. However, these algorithms are complex and have trade-offs, notably between prediction accuracy and fairness to population subgroups. This makes it hard for designers to understand algorithms and design products or services in a way that respects users' goals, values, and needs. We proposed a method to help designers and users explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their goals and needs. We evaluated our method on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts. Our evaluations show that our method can help designers and users of these systems better understand and navigate algorithmic trade-offs. This paper contributes a new way of providing designers the ability to understand and control the outcomes of algorithmic systems they are creating.2020BYBowen Yu et al.Explainable AI (XAI)AI-Assisted Decision-Making & AutomationPrivacy by Design & User ControlDIS