Data Humanism has gained prominence in personal visualization and Personal Informatics, advocating for a subjective and slow approach to engage with personal data. Collaborative sensemaking has great potential for aiding the understanding of personal data, yet little is known about addressing requirements of structure and coordination when integrating Data Humanism into collaborative visualization. In this paper, we propose design principles for creating both subjective and effective collaborative visualizations, while coordinating the slow sensemaking process and promoting data awareness and communication. We operationalize these principles into a personal visualization toolkit, which we evaluate with an observational study involving 16 university students (8 pairs) analyzing each other's screen-time data. Our findings reveal that implementing the proposed design principles: (1) facilitated data comparison from shared subjective perspectives, (2) helped coordinate sensemaking while allowing time for understanding personal data, and (3) helped the contextualization of data patterns, in turn aiding self-reflection.

Extracting concepts and understanding relationships from videos is essential in Video-Based Design (VBD), where videos serve as a primary medium for exploration but require significant effort in managing meta-information. Mind maps, with their ability to visually organize complex data, offer a promising approach for structuring and analysing video content. Recent advancements in Large Language Models (LLMs) provide new opportunities for meta-information processing and visual understanding in VBD, yet their application remains underexplored. This study recruited 28 VBD practitioners to investigate the use of prompt-tuned LLMs for generating mind maps from ethnographic videos. Comparing LLM-generated mind maps with those created by professional designers, we evaluated rated scores, design effectiveness, and user experience across two contexts. Findings reveal that LLMs effectively capture central concepts but struggle with hierarchical organization and contextual grounding. We discuss trust, customization, and workflow integration as key factors to guide future research on LLM-supported information mapping in VBD.

Despite advancements in socially aware navigation, robots still often behave inappropriately in social environments. To ensure successful application, robots must detect the human perceived appropriateness of their navigation behaviors. This paper presents a novel dataset covering a complete range of perceived appropriateness and uniquely incorporates human emotion and attention to facilitate the detection of perceived appropriateness of robot social navigation in pathways (PARSNiP). It is created based on a series of human-robot interaction experiments with 30 participants and a mobile robot. Several typical machine learning models are utilized to evaluate the dataset and analyze the contributions of different features in detecting perceived appropriateness. The results indicate that incorporating emotional and attentional features can significantly improve the accuracy of perceived appropriateness detection. There was an increase from 63% to 68% using algorithm-predicted emotional and attentional features, and a further increase to 79% with the emotion and attention data reported by the participants. With the dataset, researchers could train machine learning models to enable robots to detect perceived appropriateness accurately, fostering adaptations that improve their responsiveness and accuracy in social interactions. The dataset is available for download at https://github.com/duibcuiegiosahxois/PARSNiP.git, and videos will be shared upon request by contacting Y.Zhou-13@tudelft.nl.

The rise of large language models for client-facing conversational search in healthcare necessitates evaluation frameworks that enable the assessment and comparison of these tools. Most such frameworks centre around the automated calculation of performance-related metrics and benchmarks. Though necessary, this focus fails to account for the human factors that impact the development, use, and adoption of these systems, as well as the factors specific to the healthcare context. Human evaluation frameworks attempt to address these drawbacks, but few such frameworks have been developed so far, and even fewer are those based on expert insight. In this work, we conduct semi-structured interviews with eleven healthcare professionals in health lifestyle care. From these interviews, we contribute a two-part healthcare domain expert evaluation framework, (K) Knowledge and (I) Interaction, which organises seven evaluation metrics. Our results reveal key understudied metrics for evaluation like (I1) Context-Seeking, (I2) Empathy, and (I3) Trustworthiness.

This paper explores pair collaboration as a novel approach for making sense of personal data. Pair collaboration - characterized by dyadic comparison and structured roles for questioning and reasoning - has proven effective for co-constructing knowledge. However, current collaborative visualization tools primarily focus on group comparisons, overlooking the challenges of accommodating pair collaboration in the context of personal data. To address this gap, we propose a set of design rationales supporting subjective data analysis through dyadic comparison and mixed-focus collaboration styles for co-constructing personal narratives. We operationalize these principles in a tangible visualization toolkit, PAIRcolator. Our user study demonstrates that pairwise collaboration facilitated by the toolkit: 1) reveals detailed data insights that are effective for recalling personal experiences, and 2) fosters a structured, reciprocal sensemaking process for interpreting and reconstructing personal experiences beyond data insights. Our results shed light on the design rationales for, and the processes of pair sensemaking of personal data, and their effects to foster deep levels of reflection.

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.

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.