arXiv subjects

Johannes Ellemose

Publications and source records attributed to Johannes Ellemose.

4 recordsLinked to original sources

arXiv2026

Participatory AI: A Scandinavian Approach to Human-Centered AI

AI's transformative impact on work, education, and everyday life makes it as much a political artifact as a technological one. Current AI models are opaque, centralized, and overly generic. The algorithmic automation they provide threatens human agency and democratic values in both workplaces and daily life. To confront such challenges, we turn to Scandinavian Participatory Design (PD), which was devised in the 1970s to face a similar threat from mechanical automation. In the PD tradition, technology is seen not just as an artifact, but as a locus of democracy. Drawing from this tradition, we propose Participatory AI as a PD approach to human-centered AI that applies five PD principles to four design challenges for algorithmic automation. We use concrete case studies to illustrate how to treat AI models less as proprietary products and more as shared socio-technical systems that enhance rather than diminish human agency, human dignity, and human values.

cs.HC↗

arXiv2026

The Frustrometer: Detecting User Frustration in Data Visualization Tasks using Biomarkers and Interaction Patterns

Visualization research has largely solved \textit{how} to help a stuck or frustrated user -- through interactive onboarding, contextual help, and active guidance. The unsolved problem is \textit{when}: trigger help too eagerly and you break the user's train of thought; wait too long and they have already gone astray. We present the \textsc{Frustrometer}, a series of experiments to predict user stuckness and frustration by fusing physiological and interaction signals. The Frustrometer consists of a convolutional neural network classifier, that in real-time estimates whether user are stuck in their task or not. We collected data from a controlled study where 14 participants performed analytical tasks on two interactive visualization dashboards while we captured eye movement, pupil dilation, galvanic skin response, heart-rate, head orientation, mouse dynamics, and keyboard events. In addition participants assessed their own performance, while we annotated when during the tasks the participants were stuck. Our results reveal that autonomous physiological responses such as heart-rate and galvanic skin response provide limited insights into the frustration level of the user. Similarly, head orientations are not easily correlated with the frustrations felt by the user during visual analysis tasks. Mouse movements and gaze data conversely carry the majority of predictive signal, with mouse movements alone having a strong correlation for some participants, suggesting that lightweight instrumentation may suffice for real-time frustration detection. We end the paper by discussing how these findings can inform the design of adaptive guidance systems for complex visualization tasks that takes a multimodal approach to frustration and stuckness detection.

cs.HC↗

arXiv2025

Eye of the Beholder: Towards Measuring Visualization Complexity

Constructing expressive and legible visualizations is a key activity for visualization designers. While numerous design guidelines exist, research on how specific graphical features affect perceived visual complexity remains limited. In this paper, we report on a crowdsourced study to collect human ratings of perceived complexity for diverse visualizations. Using these ratings as ground truth, we then evaluated three methods to estimate this perceived complexity: image analysis metrics, multilinear regression using manually coded visualization features, and automated feature extraction using a large language model (LLM). Image complexity metrics showed no correlation with human-perceived visualization complexity. Manual feature coding produced a reasonable predictive model but required substantial effort. In contrast, a zero-shot LLM (GPT-4o mini) demonstrated strong capabilities in both rating complexity and extracting relevant features. Our findings suggest that visualization complexity is truly in the eye of the beholder, yet can be effectively approximated using zero-shot LLM prompting, offering a scalable approach for evaluating the complexity of visualizations. The dataset and code for the study and data analysis can be found at https://osf.io/w85a4/

cs.HC↗

arXiv2024

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time

We propose the notion of Attention-Aware Visualizations (AAVs) that track the user's perception of a visual representation over time and feed this information back to the visualization. Such context awareness is particularly useful for ubiquitous and immersive analytics where knowing which embedded visualizations the user is looking at can be used to make visualizations react appropriately to the user's attention: for example, by highlighting data the user has not yet seen. We can separate the approach into three components: (1) measuring the user's gaze on a visualization and its parts; (2) tracking the user's attention over time; and (3) reactively modifying the visual representation based on the current attention metric. In this paper, we present two separate implementations of AAV: a 2D data-agnostic method for web-based visualizations that can use an embodied eyetracker to capture the user's gaze, and a 3D data-aware one that uses the stencil buffer to track the visibility of each individual mark in a visualization. Both methods provide similar mechanisms for accumulating attention over time and changing the appearance of marks in response. We also present results from a qualitative evaluation studying visual feedback and triggering mechanisms for capturing and revisualizing attention.

cs.HC↗