Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit ToolingAudits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult, and practitioners often need to make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ecosystem of AI audit tooling to practitioner needs. While many tools are designed to help set standards and evaluate AI systems, they often fall short in supporting accountability. We outline challenges practitioners faced in their efforts to use AI audit tools and highlight areas for future tool development beyond evaluation—from harms discovery to advocacy. We conclude that the available resources do not currently support the full scope of AI audit practitioners' needs and recommend that the field move beyond tools for just evaluation and towards more comprehensive infrastructure for AI accountability.2025VOVictor Ojewale et al.Brown University , The Center for Technological Responsibility, Reimagination and Redesign(CNTR)Explainable AI (XAI)AI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityCHI
Using Speech Agents for Mood Logging within Blended Mental Healthcare: Mental Healthcare Practitioners' PerspectivesMood logging, where people track mood-related data, is commonly used to support mental healthcare. Speech agents could prove beneficial in supporting mood logging for clients. Yet we know little about how Mental Healthcare Practitioners (MHPs) view speech as a tool to support current care practices. Through a thematic analysis of semi-structured interviews with 15 MHPs, we show that MHPs see opportunities in the convenience, and the data richness that speech agents could afford. However, MHPs also saw this richness as noisy, with using speech potentially diminishing a client's focus on mood logging as an activity. MHPs were wary of overusing AI-based tools, expressing concerns around data ownership, access and privacy. We discuss the role of speech agents within blended care, outlining key considerations when using speech for mood logging in a blended mental healthcare context.2024OCOrla Cooney et al.Intelligent Voice Assistants (Alexa, Siri, etc.)Mental Health Apps & Online Support CommunitiesCUI
Between Rhetoric and Reality: Real-world Barriers to Uptake and Early Engagement in Digital Mental Health InterventionsDigital mental health interventions (DMHIs) have potential to provide effective and accessible care to entire populations, but low client uptake and engagement are significant problems. Few prior studies explore the lived experiences of non-engagers, because reaching this population is inherently difficult. We present an observational inquiry into the barriers to sign-up and early use of a DMHI, along with reasons for initial interest in the DMHI. We collected 205 online questionnaire responses and 20 interviews from self-referring participants across four healthcare ecosystems in the UK and US. Questionnaire results revealed that uncertainty about DMHI usefulness and usability were the main barriers to uptake, whereas forgetting about it, not finding time for it and not finding it useful were the main barriers to early engagement. Participants reported multiple reasons for considering the DMHI, reflecting the contextual, subjective nature of mental health. Our thematic analysis generated themes around (1) the need for human connection, (2) the impact of self-stigma on help-seeking, (3) the lack of knowledge around DMHIs and psychological therapy, (4) the desire for personally relevant care, and (5) the fluctuating, perennial nature of mental health. We discuss implications for DMHI design, implementation and future research, as well as transdisciplinary opportunities.2024JJJacinta Jardine et al.Mental Health Apps & Online Support CommunitiesDIS
Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older AdultsMusic-based reminiscence has the potential to positively impact the psychological well-being of older adults. However, the aging process and physiological changes, such as memory decline and limited verbal communication, may impede the ability of older adults to recall their memories and life experiences. Given the advanced capabilities of generative artificial intelligence (AI) systems, such as generated conversations and images, and their potential to facilitate the reminiscing process, this study aims to explore the design of generative AI to support music-based reminiscence in older adults. This study follows a user-centered design approach incorporating various stages, including detailed interviews with two social workers and two design workshops (involving ten older adults). Our work contributes to an in-depth understanding of older adults’ attitudes toward utilizing generative AI for supporting music-based reminiscence and identifies concrete design considerations for the future design of generative AI to enhance the reminiscence experience of older adults.2024YJYucheng Jin et al.Hong Kong Baptist UniversityGenerative AI (Text, Image, Music, Video)Mental Health Apps & Online Support CommunitiesReproductive & Women's HealthCHI
Human Speakers Help Machine Listeners To account For Visual Asymmetries in DialogueHuman-machine dialogue (HMD) research debates the degree to which language production in this context is egocentric or allocentric. That is, the degree to which a person might take a machine’s perspective into account. Our study aims to identify whether users produce allocentric or egocentric language within speech-based HMD when there is asymmetry in the information available to both partners. Through an adapted referential communication task, we manipulated the presence or absence of visual distractors and occlusions, similarly to previous referential tasks used in psycholinguistic research. Results show that people are sensitive to the presence of distractors and occlusions and tend to produce more informative expressions to help machine partners account for the visual asymmetries. We discuss the fndings on how allocentric production in HMD is explained by how the division of labour manifests in spoken HMD. The fndings further our understanding of the language production mechanisms in HMD.2023PPPaola Raquel Peña et al.Voice User Interface (VUI) DesignHuman-LLM CollaborationCUI
Using Thematic Analysis in Healthcare HCI at CHI: A Scoping ReviewCHI papers researching healthcare human-computer interaction (HCI) are increasingly reporting the use of "thematic analysis" (TA). TA refers to a range of flexible and evolving approaches for qualitative data analysis. Its increased use demonstrates a change in research practices, and with that the emergence of new local standards. We need to understand and reflect upon these emerging local practices, including departures from what is advocated as quality TA practice more generally. Toward this, we conducted a scoping review of a decade of CHI publications (2012 - 2021) that researched healthcare and termed their analysis approach "thematic analysis"; 78 papers reporting a total of 100 TAs were included. We contribute a description of 1) the contexts in which TA is being used, 2) the TA approaches being conducted, and 3) how TA is being reported. Drawing on this, we discuss opportunities to improve research practice when using TA in healthcare HCI.2023RBRobert Bowman et al.Trinity College DublinMental Health Apps & Online Support CommunitiesUser Research Methods (Interviews, Surveys, Observation)CHI
Investigating Clutching Interactions for Touchless Medical Imaging SystemsTouchless input could transform clinical activity by allowing health professionals direct control over medical imaging systems in a sterile manner. Currently, users face the issues of being unable to directly manipulate imaging in aseptic environments, as well as needing to touch shared surfaces in other hospital areas. Unintended input is a key challenge for touchless interaction and could be especially disruptive in medical contexts. We evaluated four clutching techniques with 34 health professionals, measuring interaction performance and interviewing them to obtain insight into their views on clutching, and touchless control of medical imaging. As well as exploring the performance of the different clutching techniques, our analysis revealed an appetite for reliable touchless interfaces, a strong desire to reduce shared surface contact, and suggested potential improvements such as combined authentication and touchless control. Our findings can inform the development of novel touchless medical systems and identify challenges for future research.2022SCSean Cronin et al.Trinity College DublinFull-Body Interaction & Embodied InputSurgical Assistance & Medical TrainingBiosensors & Physiological MonitoringCHI
Public Views on Digital COVID-19 Certificates: a Mixed Methods User StudyThe COVID-19 pandemic has led governments worldwide to introduce various measures restricting human activity and mobility. Along with the administration of COVID-19 vaccinations and rapid testing, socio-technological solutions such as digital COVID-19 certificates have been considered as a strategy to lessen these restrictions and allow the resumption of routine activities. Using a mixed methods approach – a survey (n=1008) and 27 semi-structured interviews – this study explores the attitudes of residents in the Republic of Ireland towards the idea of introducing digital COVID-19 certificates. We examine the topics of acceptability, fairness, security and privacy of COVID-related personal data, and practical considerations for implementation. Our study reveals the conditional and contextual nature of the acceptability of digital certificates, identifying specific factors that affect it, associated data practices, and related public concerns and expectations of such technologies.2022LNLeysan Nurgalieva et al.Aalto UniversityPrivacy by Design & User ControlPrivacy Perception & Decision-MakingSustainable HCICHI
The TAC Toolkit: Supporting Design for User Acceptance of Health Technologies from a Macro-Temporal PerspectiveUser acceptance is key for the successful uptake and use of health technologies, but also impacted by numerous factors not always easily accessible nor operationalised by designers in practice. This work seeks to facilitate the application of acceptance theory in design practice through the Technology Acceptance (TAC) toolkit: a novel theory-based design tool and method comprising 16 cards, 3 personas, 3 scenarios, a virtual think-space, and a website, which we evaluated through workshops conducted with 21 designers of health technologies. Findings showed that the toolkit revised and extended designers' knowledge of technology acceptance, fostered their appreciation, empathy and ethical values while designing for acceptance, and contributed towards shaping their future design practice. We discuss implications for considering user acceptance a dynamic, multi-stage process in design practice, and better supporting designers in imagining distant acceptance challenges. Finally, we examine the generative value of the TAC toolkit and its possible future evolution.2022CNCamille Nadal et al.Trinity College DublinMental Health Apps & Online Support CommunitiesPrototyping & User TestingCHI
Using the Geneva Emotion Wheel to Measure Perceived Affect in Human-Robot InteractionThe ability to clearly communicate a wide range of emotional states is considered a desirable trait for social robots. This research proposes that the Geneva Emotion Wheel (GEW), a self-report instrument for measuring emotional reactions, has strong potential for use as a tool for evaluating the expression of affective content by robots. Factors that make the GEW advantageous over existing evaluation methods include: ease of administration, reduction in the importance of word labels, and coverage of ‘no emotion’ states. Statistical analyses of the GEW are proposed, isolating quantitative metrics of emotion distinctness. An experiment requiring participants to rate the perceived emotion of a social robot was conducted, employing the proposed methods. Analysis using the GEW revealed significant differences in the reliability of different expressions to clearly convey emotional states. The GEW provided a repeatable, systematic framework for estimating perceived affect of robot expression. Thus, the results suggest the GEW offers a powerful tool for design purposes as well as analysis. To support future research using the GEW, the software used for the analysis has been packaged and made available as an open-source resource to the community.2020ACAdam Kavanagh Coyne et al.Agent Personality & AnthropomorphismSocial Robot InteractionHRI
What should robots feel like? Factors that Influence the Perception of Robot-SkinIt’s widely accepted that a robot’s embodiment plays an important role during human-robot interaction (HRI). While many studies have explored the effect of robot appearance, relatively little is known about how the texture and stiffness of the surface material, or what may be referred to as ‘robot-skin’, influences how the robot is perceived. Gaining improved understanding in this area may have direct and actionable consequences on robot design, since at present nearly all commercially available service robots have similar exterior surfaces composed of smooth, stiff materials, usually plastic. This study is framed around systematically investigating the type of textures that may be better suited for these robots. First, experiments were undertaken to classify the textural characteristics of 27 distinct materials which could potentially be used as a robotskin. A representative subset of these materials was then selected for a second experiment that explored how the stiffness and tactile properties of the material influenced its perceived suitability for use on a service robot. The research found that people strongly preferred surface textures that were soft, rather than stiff. The most suitable material stiffness was found to be context dependent; soft options were preferred in the blind test condition, but for cases where participants were presented with the 3D image of a service robot in an immersive virtual reality environment, medium stiffness materials were preferred. In the final part of the study, we identified a range of textural properties that seem to correlate with high and low suitability for use on service robots. It is hoped that these findings are useful to help inform the design of future HRI systems, and motivate further investigation into the social roles of robot-skin.2020CMConor McGinn et al.Shape-Changing Interfaces & Soft Robotic MaterialsSocial Robot InteractionHRI
The Experience of Guided Online Therapy: A Longitudinal, Qualitative Analysis of Client Feedback in a Naturalistic RCTInternet-delivered Cognitive Behavioural Therapy (iCBT) is an effective treatment for depression and anxiety disorders. However longitudinal qualitative research into the client's subjective experience of this form of treatment 'in the wild' is relatively scarce. We present an analysis of secondary outcomes in a naturalistic RCT conducted within the UK's Improving Access to Psychological Therapies programme. We evaluated clients' expectations, experience, and context of usage of iCBT, across three timepoints. Results are discussed in terms of the creation of a therapeutic space online, the impact of hope, expectations and personal factors on the therapeutic experience, iCBT as "therapy on the go" and developing skills for life. While iCBT on the whole provides a positive, supportive and therapeutic experience for clients, the study identified managing expectations, polarized preferences, momentary help-seeking and long-term support as important aspects of the experience to consider in future design.2020JJJacinta Jardine et al.SilverCloud HealthMental Health Apps & Online Support CommunitiesCHI
Understanding Client Support Strategies to Improve Clinical Outcomes in an Online Mental Health InterventionOnline mental health interventions are increasingly important in providing access to, and supporting the effectiveness of, mental health treatment. While these technologies are effective, user attrition and early disengagement are key challenges. Evidence suggests that integrating a human supporter into such services mitigates these challenges, however, it remains under-studied how supporter involvement benefits client outcomes, and how to maximize such effects. We present our analysis of 234,735 supporter messages to discover how different support strategies correlate with clinical outcomes. We describe our machine learning methods for: (i) clustering supporters based on client outcomes; (ii) extracting and analyzing linguistic features from supporter messages; and (iii) identifying context-specific patterns of support. Our findings indicate that concrete, positive and supportive feedback from supporters that reference social behaviors are strongly associated with better outcomes; and show how their importance varies dependent on different client situations. We discuss design implications for personalized support and supporter interfaces.2020PCPrerna Chikersal et al.Carnegie Mellon UniversityElectrical Muscle Stimulation (EMS)Agent Personality & AnthropomorphismMental Health Apps & Online Support CommunitiesCHI
Tabloidization versus Credibility: Short Term Gain for Long Term PainPrint news agencies have been under pressure from falling sales and advertising revenue and increased competition. As the Internet became the dominant medium, news agencies invested heavily in their websites and apps, providing their news for free, rather than selling a print edition. Reducing the cost of production and removing access barriers such as geographic location had the potential to increase readership and advertising, covering costs and maintaining profits. Unfortunately, this business model has for the most part failed. Many higher quality news agencies are now implementing paywalls on their news websites to once again monetize their product. Others have begun to emulate the look and feel of tabloid news websites to increase readership and stickiness and advertising revenue. This study shows the negative impact of such visual tabloidization on initial impressions of credibility, which may have long term detrimental effects on the news agency.<br>The authors would like to dedicate this paper to the memory of Professor Séamus "Shay" Lawless, the supervisor of this work who died on May 16th 2019 after fulfilling his dream of summiting Mount Everest.2020BSBrendan Spillane et al.Trinity College DublinSocial Platform Design & User BehaviorMisinformation & Fact-CheckingCHI
Managing Multimorbidity: Identifying Design Requirements for a Digital Self-Management Tool to Support Older Adults with Multiple Chronic ConditionsOlder adults with multiple chronic conditions (multimorbidity) face complex self-management routines, including symptom monitoring, managing multiple medications, coordinating healthcare visits, communicating with multiple healthcare providers and processing and managing potentially conflicting advice on conditions. While much research exists on single disease management, little, if any research has explored the topic of technology to support those with multimorbidity, particularly older adults, to self-manage with support from a care network. This paper describes a large qualitative study with 125 participants, including older adults with multimorbidity and those who care for them, across two European countries. Key findings related to the: impact of multimorbidity, complexities involved in self-management, motivators and barriers to self-management, sources of support and poor communication as a barrier to care coordination. We present important concepts and design features for a digital health system that aim to address requirements derived from this study.2019JDJulie Doyle et al.Dundalk Institute of TechnologyChronic Disease Self-Management (Diabetes, Hypertension, etc.)Elderly Care & Dementia SupportCHI
Exploring and Designing for Memory Impairments in DepressionDepression is an affective disorder with distinctive autobiographical memory impairments, including negative bias, overgeneralization and reduced positivity. Several clinical therapies address these impairments, and there is an opportunity to develop new supports for treatment by considering depression-associated memory impairments within design. We report on interviews with ten experts in treating depression, with expertise in both neuropsychology and cognitive behavioral therapies. The interviews explore approaches for addressing each of these memory impairments. We found consistent use of positive memories for treating all memory impairments, the challenge of direct retrieval, and the need to support the experience of positive memories. We aim to sensitize HCI researchers to the limitations of memory technologies, broaden their awareness of memory impairments beyond episodic memory recall, and inspire them to engage with this less explored design space. Our findings open up new design opportunities for memory technologies for depression, including positive memory banks for active encoding and selective retrieval, novel cues for supporting generative retrieval, and novel interfaces to strengthen the reliving of positive memories.2019CQChengcheng Qu et al.Lancaster UniversityCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI
Engagement with Mental Health Screening on Mobile Devices: Results from an Antenatal Feasibility StudyPerinatal depression (PND) affects up to 15% of women within the United Kingdom and has a lasting impact on a woman's quality of life, birth outcomes and her child's development. Suicide is the leading cause of maternal mortality. However, it is estimated that at least 50% of PND cases go undiagnosed. This paper presents the results of the first feasibility study to examine the potential of mobile devices to engage women in antenatal mental health screening. Using a mobile application, 254 women attending 14 National Health Service midwifery clinics provided 2,280 momentary and retrospective reports of their wellbeing over a 9-month period. Women spoke positively of the experience, installing and engaging with this technology regardless of age, education, wellbeing, number of children, marital or employment status, or past diagnosis of depression. 39 women reported a risk of depression, self-harm or suicide; two-thirds of whom were not identified by screening in-clinic.2019KDGavin Doherty et al.Trinity College DublinMental Health Apps & Online Support CommunitiesCHI
HCI and Affective Health: Taking stock of a decade of studies and charting future research directionsIn the last decade, the number of articles on HCI and health has increased dramatically. We extracted 139 papers on depression, anxiety and bipolar health issues from 10 years of SIGCHI conference proceedings. 72 of these were published in the last two years. A systematic analysis of this growing body of literature revealed that most innovation happens in automated diagnosis, and self-tracking, although there are innovative ideas in tangible interfaces. We noted an overemphasis on data production without consideration of how it leads to fruitful interventions. Moreover, we see a need to promote ethical practices for involvement of people living with affective disorders. Finally, although only 16 studies evaluate technologies in a clinical context, several forms of support and intervention illustrate how rich insights are gained from evaluations with real patients. Our findings highlight potential for growth in the design space of affective health technologies.2019PSPedro Sanches et al.KTH Royal Institute of Technology in StockholmMental Health Apps & Online Support CommunitiesCHI
What Makes a Good Conversation? Challenges in Designing Truly Conversational AgentsConversational agents promise conversational interaction but fail to deliver. Efforts often emulate functional rules from human speech, without considering key characteristics that conversation must encapsulate. Given its potential in supporting long-term human-agent relationships, it is paramount that HCI focuses efforts on delivering this promise. We aim to understand what people value in conversation and how this should manifest in agents. Findings from a series of semi-structured interviews show people make a clear dichotomy between social and functional roles of conversation, emphasising the long-term dynamics of bond and trust along with the importance of context and relationship stage in the types of conversations they have. People fundamentally questioned the need for bond and common ground in agent communication, shifting to more utilitarian definitions of conversational qualities. Drawing on these findings we discuss key challenges for conversational agent design, most notably the need to redefine the design parameters for conversational agent interaction.2019LCLeigh Clark et al.University College DublinConversational ChatbotsAgent Personality & AnthropomorphismCHI