Public sector organizations increasingly use artificial intelligence to augment, support, and automate decision-making. However, such public AI can potentially infringe on citizens’ right to autonomy. Contestability is a system quality that protects against this by ensuring systems are open and responsive to disputes throughout their life cycle. While a growing body of work is investigating contestable AI by design, little of this knowledge has so far been evaluated with practitioners. To make explicit the guiding ideas underpinning contestable AI research, we construct the generative metaphor of the Agonistic Arena, inspired by the political theory of agonistic pluralism. Combining this metaphor and current contestable AI guidelines, we develop an infographic supporting the early-stage concept design of public AI system contestability mechanisms. We evaluate this infographic in five workshops paired with focus groups with a total of 18 practitioners, yielding ten concept designs. Our findings outline the mechanisms for contestability derived from these concept designs. Building on these findings, we subsequently evaluate the efficacy of the Agonistic Arena as a generative metaphor for the design of public AI and identify two competing metaphors at play in this space: the Black Box and the Sovereign.@en

The increased ubiquity of 360° video presents a unique opportunity for designers to deeply engage with the world of users by capturing the complete visual context. However, the opportunities and challenges 360° video introduces for video design ethnography are unclear. This study investigates this gap through 16 workshops in which experienced designers engaged with 360° video. Our analysis shows that while 360° video enhances designers’ ability to explore and understand user contexts, it also complicates the process of sharing insights. To address this challenge, we present two opportunities to support the use of 360° video by designers - the creation of designerly 360° video annotation tools, and 360° “screenshots” - in order to enable designers to leverage the complete context of 360° video for user research.@en

eHealth development faces the challenge of generating evidence about health effectiveness in real-world settings. Designers can potentially support this challenge but must understand health approaches to evidence generation about health outcomes. This case study investigates how health and care professionals conceptualise health outcomes and their evidence generation in eHealth. Our results identify three key conceptual dimensions: effect, meaning, and collection. We discuss how these inform future design competencies to support evidence generation about health outcomes in eHealth design.@en

Most people interact with digital technologies that collect personal data about their behavior and experiences, leaving behind a data trail. The data within this trail is abstract and difficult to interpret; still, people often need to decide about its collection and distribution. Hence, it is paramount to support personal data literacy, for which data visualization approaches have been successful. These approaches focus mostly on data from single sources (e.g., IoT devices at home) or types (e.g., menstrual logs) and fail to capture people’s situated knowledge. We hypothesize that creating data comics can address these limitations and support people in developing personal data literacy. In this paper, we explore how non-data experts create personal data comics, starting from simple data visualizations, and investigate their effectiveness and engagement in the context of pregnancy. Doing so, we identify comic elements that facilitate the autonomous exploration of personal data and provide design recommendations to support independent data comic creation.@en

Video-Based Design (VBD) is a design methodology that utilizes video as a primary tool for understanding user interactions, prototyping, and conducting research to enhance the design process. Artificial Intelligence (AI) can be instrumental in video-based design by analyzing and interpreting visual data from videos to enhance user interaction, automate design processes, and improve product functionality. In this study, we explore how AI can enhance professional video-based design with a State-of-the-Art (SOTA) deep learning model. We developed a prototype annotation platform (MarkupLens) and conducted a between-subjects eye-tracking study with 36 designers, annotating videos with three levels of AI assistance. Our findings indicate that MarkupLens improved design annotation quality and productivity. Additionally, it reduced the cognitive load that designers exhibited and enhanced their User Experience (UX). We believe that designer-AI collaboration can greatly enhance the process of eliciting insights in video-based design.@en

Data donation is an emerging practice enabling personal data collection for research. While it offers opportunities to access new insights into people’s behavior and experiences through their digital-trace data, the role of individuals – as research participants – is limited in most data donation projects. They primarily contribute with data, limiting the perspectives included and accounted for around critical research-design decisions. In this paper, we explore the opportunity to embed data donation in research processes that are not only contributory but collaborative and co-created. To do so, we present a participatory data donation case study focused on athletes’ perceptions of the impact of their menstrual cycle on their sports performance through their physical activity data. Based on the data donation experiences of 20 athletes, our paper provides insights into people’s preferences and expectations in participatory data donation processes and discusses considerations for supporting various degrees of participation in future data donation research.@en

In social environment navigation, robots inevitably exhibit behaviors that are perceived as inappropriate by humans. Current robots lack the ability to adapt to such human perceptions, leading to repeated inappropriate behaviors. This study employs a mixed-methods approach to explore human-preferred robot adaptations, combining qualitative data from a series of human-robot interactions and a semi-structured interview, and quantitative data from an online survey. 12 participants were recruited to interact with a mobile robot in an indoor setting, reporting 139 instances of inappropriate robot behaviors. The subsequent semi-structured interviews regarding these instances yielded 9 types of inappropriate behaviors and 10 major types of human-preferred robot adaptations, ranging from general ones, such as stopping the motion, to more specific ones, like moving away and then stopping. Additionally, 12 human-preferred adaptations were selected from the interview data and presented to the same participants through an online survey to evaluate their effectiveness in addressing the inappropriate behaviors previously identified. The results reveal the human preference for the robot to move to the side and then stop in most scenarios, which might serve as a general adaptation for addressing inappropriate robot navigation behaviors.@en

The design of collaborative personal informatics (PI) has shifted its focus from using one’s own data to integrating others’ data to enhance self-understanding. In this trend, understanding the effectiveness of the two data sources in facilitating personal insights becomes essential, as a comprehensive understanding of self-understanding requires insights from both individual and interpersonal perspectives. While recent studies have suggested the potential role of others’ data as a reflective medium to generate personal insights, little is understood about its distinctive effectiveness in personal insights generated compared to one’s own data. To address this gap, we conducted a crowdsourced study involving two participant groups (N1=N2=60) in a data-informed reflection task: Data Providers (DP) reflecting on their own data; Non-Data Providers (NDP) reflecting on the data provided by DP. Analyzing the textual responses, we assess the reflection levels, self-disclosure levels, and characteristics of personal insights. Our findings uncover that others’ data possess a comparable effectiveness in facilitating reflection and self-disclosure of personal thoughts and feelings. Others’ data displays a strength in supporting value judgments, while one’s own data excels in enhancing behavioral awareness. This research sheds light on the design of collaborative PI, offering insights into how to leverage the benefits while mitigating the disadvantages of both data sources to enhance the self-understanding.

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Ideation is a critical component of video-based design (VBD), where videos serve as the primary medium for design exploration and inspiration. The emergence of generative AI offers considerable potential to enhance this process by streamlining video analysis and facilitating idea generation. In this paper, we present DesignMinds, a prototype that integrates a state-of-the-art Vision-Language Model (VLM) with a context-enhanced Large Language Model (LLM) to support ideation in VBD. To evaluate DesignMinds, we conducted a between-subject study with 35 design practitioners, comparing its performance to a baseline condition. Our results demonstrate that DesignMinds significantly enhances the flexibility and originality of ideation, while also increasing task engagement. Importantly, the introduction of this technology did not negatively impact user experience, technology acceptance, or usability.@en

Most people are entangled with an ever-growing trail of data that results from their daily interactions with products and services. Yet, they are hardly aware of the nature and characteristics of the data within this trail. We design dataslip, a provocative artifact that materializes the personal data trail into a receipt and aims to elicit creepiness. We demonstrate dataslip at two events in Delft, The Netherlands. Dataslip is a starting point to foster conversations with local community members about the underlying challenges and potential alternatives to personal data collection and use. We use these as prompts for further speculation through a collaborative futuring exercise with children, where we part from challenges towards hopeful and empowering futures. We contribute with an artifact that invites individuals to interrogate the current personal data practices they are embedded in and a set of five speculative design scenarios that suggest hopeful and empowering alternatives.@en

Public sector organizations increasingly use artificial intelligence to augment, support, and automate decision-making. However, such public AI can potentially infringe on citizens’ right to autonomy. Contestability is a system quality that protects against this by ensuring systems are open and responsive to disputes throughout their life cycle. While a growing body of work is investigating contestable AI by design, little of this knowledge has so far been evaluated with practitioners. To make explicit the guiding ideas underpinning contestable AI research, we construct the generative metaphor of the Agonistic Arena, inspired by the political theory of agonistic pluralism. Combining this metaphor and current contestable AI guidelines, we develop an infographic supporting the early-stage concept design of public AI system contestability mechanisms. We evaluate this infographic in five workshops paired with focus groups with a total of 18 practitioners, yielding ten concept designs. Our findings outline the mechanisms for contestability derived from these concept designs. Building on these findings, we subsequently evaluate the efficacy of the Agonistic Arena as a generative metaphor for the design of public AI and identify two competing metaphors at play in this space: the Black Box and the Sovereign.@en

Data donation is an emerging practice for collecting personal data. However, recent data donation approaches are insufficient in intimate research contexts as they perceive data as neutral and objective and do not consider the contexts where data is generated and shaped nor offer choices beyond whether to disclose data. In this paper, we investigate how Data Feminism can inform an alternative form of data donation and propose the Sensitive Data Donation (sDD) method. It recognizes the sensitive nature of data and assumes the importance of situating and contextualizing it through balanced participation from donors, either as contributors, collaborators, or co-creators. To develop the method, we conduct a scoping literature review where we conceptualize data donation theories and practices. These serve as a base to critique recent approaches and propose an alternative: sDD. It comprises five principles integrated into a five-phase approach. We conclude by discussing its limitations and future challenges.@en

Public sector organizations increasingly use artificial intelligence to augment, support, and automate decision-making. However, such public AI can potentially infringe on citizens’ right to autonomy. Contestability is a system quality that protects against this by ensuring systems are open and responsive to disputes throughout their life cycle. While a growing body of work is investigating contestable AI by design, little of this knowledge has so far been evaluated with practitioners. To make explicit the guiding ideas underpinning contestable AI research, we construct the generative metaphor of the Agonistic Arena, inspired by the political theory of agonistic pluralism. Combining this metaphor and current contestable AI guidelines, we develop an infographic supporting the early-stage concept design of public AI system contestability mechanisms. We evaluate this infographic in five workshops paired with focus groups with a total of 18 practitioners, yielding ten concept designs. Our findings outline the mechanisms for contestability derived from these concept designs. Building on these findings, we subsequently evaluate the efficacy of the Agonistic Arena as a generative metaphor for the design of public AI and identify two competing metaphors at play in this space: the Black Box and the Sovereign.

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Digital technologies have increasingly integrated into people's lives, continuously capturing their behavior through potentially sensitive data. In the context of voice assistants, there is a misalignment between experts, regulators, and users on whether and what data is 'sensitive', partly due to how data is presented to users; as single interactions. We investigate users' perspectives on the sensitivity and intimacy of their Google Assistant speech records, introduced comprehensively as single interactions, patterns, and inferences. We collect speech records through data donation and explore them in collaboration with 17 users during interviews based on predefined data-sharing scenarios. Our results indicate a tipping point in perceived sensitivity and intimacy as participants delve deeper into their data and the information derived from it. We propose a conceptualization of sensitivity and intimacy that accounts for the fuzzy nature of data and must disentangle from it. We discuss the implications of our findings and provide recommendations.

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As we navigate physical (e.g., supermarket) and digital (e.g., social media) systems, we generate personal data about our behavior. Researchers and designers increasingly rely on this data and appeal to several approaches to collect it. One of these is data donation, which encourages people to voluntarily transfer their (personal) data collected by external parties to a specific cause. One of the central pillars of data donation is informed consent, meaning people should be adequately informed about what and how their data will be used. However, can we be adequately informed when it comes to donating our data when many times we don’t even know it is being collected and, even more so, what exactly is being collected? In this paper, we investigate how to foster (personal) data literacy and increase donors’ understanding of their data. We introduce a Research through Design approach where we define a data donation journey in the context of speech records, data collected by Google Assistant. Based on the data donation experiences of 22 donors, we propose a data donation framework that understands and approaches data donation as an encompassing process with mutual benefit for donors and researchers. Our framework supports a donation process that dynamically and iteratively engages donors in exploring and understanding their data and invites them to (re)evaluate and (re)assess their participation. Through this process, donors increase their data literacy and are empowered to give meaningfully informed consent.

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Local governments increasingly use artificial intelligence (AI) for automated decision-making. Contestability, making systems responsive to dispute, is a way to ensure they respect human rights to autonomy and dignity. We investigate the design of public urban AI systems for contestability through the example of camera cars: human-driven vehicles equipped with image sensors. Applying a provisional framework for contestable AI, we use speculative design to create a concept video of a contestable camera car. Using this concept video, we then conduct semi-structured interviews with 17 civil servants who work with AI employed by a large northwestern European city. The resulting data is analyzed using reflexive thematic analysis to identify the main challenges facing the implementation of contestability in public AI. We describe how civic participation faces issues of representation, public AI systems should integrate with existing democratic practices, and cities must expand capacities for responsible AI development and operation.@en

Recently, methods and approaches such as Participatory Data Analysis, Data-Enabled Design, and Contextual Inquiry have highlighted how design activities can benefit from behavioral data. This data offers new ways to learn from what people do and how they do it, across time and space. However, behavioral data introduces changes and frictions to design activities and poses several challenges for designers to overcome. In this paper, we conduct two workshops with 18 expert designers, from industry and academia, to understand the nature of these challenges, beyond the technical aspects. We contribute by underlining the challenges and opportunities of incorporating behavioral data into design activities; including a design perspective on data, interacting with participants, and interacting with regulatory bodies. We translate our findings into opportunities for a better alignment between regulatory bodies, designers, and participants. We propose to harness the iterative nature of design activities and embedded it into a process that allows for continuous reflection, reassessment, and review of highly dynamic datasets.@en

The Future of Design Education working group on doctoral education included doctoral supervisors from nine programs around the world and addressed the indeterminacy of standards for the PhD in Design. Internationally, “contributions to knowledge” under the PhD degree title range from evidence-based investigations documented in a dissertation to personal reflections on making artifacts. In some programs, quantitative and qualitative research methods are taught; in others, there is no instruction in methods. The working group suggested that reflection on one's own creative production is the role of the professional master's degree and recommended standards for two doctoral programs—the PhD and the Doctor of Design (DDes). The group defined the PhD as addressing unresolved problems with the goal of generalizable knowledge or theory for the field. It described the DDes as a professional practice degree in which research is done in a practice setting to frame a specific opportunity space, guide in-process design decisions, or evaluate outcomes. DDes findings do not claim generalizability and result in “cases.” The working group discussed methods, sampling, standards of evidence and claims, ethics, research writing, and program management.

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Evidence-based practices play an essential role in the development of eHealth systems. Prior research has investigated the challenge of shared understanding between professionals from the fields of health sciences and design and has highlighted the need for effective alignment of development and research practices in eHealth. However, there is a limited understanding of epistemological differences between these fields and how professionals conceptualise evidence. In this paper, we investigate how healthcare and design professionals think about evidence and how they implement evidence practices in their work. We interviewed eight professionals and used reflexive thematic analysis to identify the challenges and strategies associated with their evidence practices. Our results identify five shared evidence practices between healthcare and design professionals: stakeholder-driven, process-driven, problem-driven, effect-driven, and solution-driven. These five evidence practices indicate opportunities for closer alignment of development and research practices among healthcare and design professionals and offer a basis to create a shared understanding of evidence between both fields.@en