EuroVis24: Eurographics Conference on Visualization

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Generating Euler Diagrams Through Combinatorial Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Rottmann, Peter; Rodgers, Peter; Yan, Xinyuan; Archambault, Daniel; Wang, Bei; Haunert, Jan-Henrik; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP-hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity-constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non-trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.
Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Eulzer, Pepe; Richter, Kevin; Hundertmark, Anna; Wickenhoefer, Ralph; Klingner, Carsten; Lawonn, Kai; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The emergence of computational fluid dynamics (CFD) enabled the simulation of intricate transport processes, including flow in physiological structures, such as blood vessels. While these so-called hemodynamic simulations offer groundbreaking opportunities to solve problems at the clinical forefront, a successful translation of CFD to clinical decision-making is challenging. Hemodynamic simulations are intrinsically complex, time-consuming, and resource-intensive, which conflicts with the timesensitive nature of clinical workflows and the fact that hospitals usually do not have the necessary resources or infrastructure to support CFD simulations. To address these transfer challenges, we propose a novel visualization system which enables instant flow exploration without performing on-site simulation. To gain insights into the viability of the approach, we focus on hemodynamic simulations of the carotid bifurcation, which is a highly relevant arterial subtree in stroke diagnostics and prevention. We created an initial database of 120 high-resolution carotid bifurcation flow models and developed a set of similarity metrics used to place a new carotid surface model into a neighborhood of simulated cases with the highest geometric similarity. The neighborhood can be immediately explored and the flow fields analyzed.We found that if the artery models are similar enough in the regions of interest, a new simulation leads to coinciding results, allowing the user to circumvent individual flow simulations. We conclude that similarity-based visual analysis is a promising approach toward the usability of CFD in medical practice.
From Delays to Densities: Exploring Data Uncertainty through Speech, Text, and Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Stokes, Chase; Sanker, Chelsea; Cogley, Bridget; Setlur, Vidya; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Understanding and communicating data uncertainty is crucial for making informed decisions in sectors like finance and healthcare. Previous work has explored how to express uncertainty in various modes. For example, uncertainty can be expressed visually with quantile dot plots or linguistically with hedge words and prosody. Our research aims to systematically explore how variations within each mode contribute to communicating uncertainty to the user; this allows us to better understand each mode's affordances and limitations. We completed an exploration of the uncertainty design space based on pilot studies and ran two crowdsourced experiments examining how speech, text, and visualization modes and variants within them impact decision-making with uncertain data. Visualization and text were most effective for rational decision-making, though text resulted in lower confidence. Speech garnered the highest trust despite sometimes leading to risky decisions. Results from these studies indicate meaningful trade-offs among modes of information and encourage exploration of multimodal data representations.
DynTrix: A Hybrid Representation for Dynamic Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Vago, Benjamin; Archambault, Daniel; Arleo, Alessio; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Hybrid graph representations combine two or more network visualization techniques in a unique drawing, simultaneously leveraging their strong traits. Since their introduction in the early 2000s, hybrid representations have gained significant research interest, with the introduction of new techniques and comparative user studies. However, all this research has not considered dynamic graphs. In this paper, we investigate hybrid graph representations in a dynamic network context and present DynTrix. Our system uses the NodeTrix representation as a basis, but the research extends this representation to the dynamic network domain. DynTrix supports automatic or manually created clusters/matrices across time. Drawing stability is implemented through aggregation and users can rearrange the nodes/matrix positions and pin them. DynTrix visualizes the temporal dynamics of the network through a combination of movement and element highlighting. We also introduce the concept of volatility, that allows the identification of actors in the network that are the most volatile. Matrices can be ordered such that stable cores gravitate towards the centre of the matrix. We integrate this technique in a visual analytics application for the exploration of offline dynamic networks and evaluate our system through case studies and qualitative expert interviews. Experts agree on the capabilities of the system, noting its potential for the analysis of dynamic networks through hybrid representations.
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Liu, Shusen; Miao, Haichao; Li, Zhimin; Olson, Matthew; Pascucci, Valerio; Bremer, Peer-Timo; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
With recent advances in multi-modal foundation models, the previously text-only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented opportunities for various applications in visualization. Compared to existing work on LLM-based visualization works that generate and control visualization with textual input and output only, the proposed approach explores the utilization of the visual processing ability of multi-modal LLMs to develop Autonomous Visualization Agents (AVAs) that can evaluate the generated visualization and iterate on the result to accomplish user-defined objectives defined through natural language. We propose the first framework for the design of AVAs and present several usage scenarios intended to demonstrate the general applicability of the proposed paradigm. Our preliminary exploration and proof-of-concept agents suggest that this approach can be widely applicable whenever the choices of appropriate visualization parameters require the interpretation of previous visual output. Our study indicates that AVAs represent a general paradigm for designing intelligent visualization systems that can achieve high-level visualization goals, which pave the way for developing expert-level visualization agents in the future.
Persist: Persistent and Reusable Interactions in Computational Notebooks
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Gadhave, Kiran; Cutler, Zach; Lex, Alexander; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Computational notebooks, such as Jupyter, support rich data visualization. However, even when visualizations in notebooks are interactive, they are a dead end: Interactive data manipulations, such as selections, applying labels, filters, categorizations, or fixes to column or cell values, could be efficiently applied in interactive visual components, but interactive components typically cannot manipulate Python data structures. Furthermore, actions performed in interactive plots are lost as soon as the cell is re-run, prohibiting reusability and reproducibility. To remedy this problem, we introduce Persist, a family of techniques to (a) capture interaction provenance, enabling the persistence of interactions, and (b) map interactions to data manipulations that can be applied to dataframes.We implement our approach as a JupyterLab extension that supports tracking interactions in Vega- Altair plots and in a data table view. Persist can re-execute interaction provenance when a notebook or a cell is re-executed, enabling reproducibility and re-use.We evaluate Persist in a user study targeting data manipulations with 11 participants skilled in Python and Pandas, comparing it to traditional code-based approaches. Participants were consistently faster and were able to correctly complete more tasks with Persist.
HORA 3D: Personalized Flood Risk Visualization as an Interactive Web Service
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Rauer-Zechmeister, Silvana; Cornel, Daniel; Sadransky, Bernhard; Horváth, Zsolt; Konev, Artem; Buttinger-Kreuzhuber, Andreas; Heidrich, Raimund; Blöschl, Günter; Gröller, Eduard; Waser, Jürgen; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
We propose an interactive web-based application to inform the general public about personal flood risks. Flooding is the natural hazard affecting most people worldwide. Protection against flooding is not limited to mitigation measures, but also includes communicating its risks to affected individuals to raise awareness and preparedness for its adverse effects. Until now, this is mostly done with static and indiscriminate 2D maps of the water depth. These flood hazard maps can be difficult to interpret and the user has to derive a personal flood risk based on prior knowledge. In addition to the hazard, the flood risk has to consider the exposure of the own house and premises to high water depths and flow velocities as well as the vulnerability of particular parts. Our application is centered around an interactive personalized visualization to raise awareness of these risk factors for an object of interest. We carefully extract and show only the relevant information from large precomputed flood simulation and geospatial data to keep the visualization simple and comprehensible. To achieve this goal, we extend various existing approaches and combine them with new real-time visualization and interaction techniques in 3D. A new view-dependent focus+context design guides user attention and supports an intuitive interpretation of the visualization to perform predefined exploration tasks. HORA 3D enables users to individually inform themselves about their flood risks. We evaluated the user experience through a broad online survey with 87 participants of different levels of expertise, who rated the helpfulness of the application with 4.7 out of 5 on average.
Antarstick: Extracting Snow Height From Time-Lapse Photography
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lang, Matěj; Mráz, Radoslav; Trtík, Marek; Stoppel, Sergej; Byška, Jan; Kozlikova, Barbora; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The evolution and accumulation of snow cover are among the most important characteristics influencing Antarctica's climate and biotopes. The changes in Antarctica are also substantially impacting global climate change. Therefore, detailed monitoring of snow evolution is key to understanding such changes. One way to conduct this monitoring is by installing trail cameras in a particular region and then processing the captured information. This option is affordable, but has some drawbacks, such as the fully automatic solution for the extraction of snow height from these images is not feasible. Therefore, it still requires human intervention, manually correcting the inaccurately extracted information. In this paper, we present Antarstick, a tool for visual guidance of the user to potentially wrong values extracted from poor-quality images and support for their interactive correction. This tool allows for much quicker and semi-automated processing of snow height from time-lapse photography.
CAN: Concept-aligned Neurons for Visual Comparison of Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Mingwei; Jeong, Sangwon; Liu, Shusen; Berger, Matthew; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
We present concept-aligned neurons, or CAN, a visualization design for comparing deep neural networks. The goal of CAN is to support users in understanding the similarities and differences between neural networks, with an emphasis on comparing neuron functionality across different models. To make this comparison intuitive, CAN uses concept-based representations of neurons to visually align models in an interpretable manner. A key feature of CAN is the hierarchical organization of concepts, which permits users to relate sets of neurons at different levels of detail. CAN's visualization is designed to help compare the semantic coverage of neurons, as well as assess the distinctiveness, redundancy, and multi-semantic alignment of neurons or groups of neurons, all at different concept granularity. We demonstrate the generality and effectiveness of CAN by comparing models trained on different datasets, neural networks with different architectures, and models trained for different objectives, e.g. adversarial robustness, and robustness to out-of-distribution data.
Transmittance-based Extinction and Viewpoint Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Himmler, Paul; Günther, Tobias; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
A long-standing challenge in volume visualization is the effective communication of relevant spatial structures that might be hidden due to occlusions. Given a scalar field that indicates the importance of every point in the domain, previous work synthesized volume visualizations by weighted averaging of samples along view rays or by optimizing a spatially-varying extinction field through an energy minimization. This energy minimization, however, did not directly measure the contribution of an individual sample to the final pixel color. In this paper, we measure the visibility of relevant structures directly by incorporating the transmittance into a non-linear energy minimization. For the first time, we not only perform a transmittance-based extinction optimization, we concurrently optimize the camera position to find ideal viewpoints. We derive the partial derivatives for the gradient-based optimization symbolically, which makes the application of automatic differentiation methods unnecessary. The transmittance-based formulation gives a direct visibility measure that is communicated to the user in order to make aware of potentially overlooked relevant structures. Our approach is compatible with any measure of importance and its versatility is demonstrated in multiple data sets.
Depth for Multi-Modal Contour Ensembles
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Chaves-de-Plaza, Nicolas F.; Molenaar, Mathijs; Mody, Prerak; Staring, Marius; Egmond, René van; Eisemann, Elmar; Vilanova, Anna; Hildebrandt, Klaus; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The contour depth methodology enables non-parametric summarization of contour ensembles by extracting their representatives, confidence bands, and outliers for visualization (via contour boxplots) and robust downstream procedures. We address two shortcomings of these methods. Firstly, we significantly expedite the computation and recomputation of Inclusion Depth (ID), introducing a linear-time algorithm for epsilon ID, a variant used for handling ensembles with contours with multiple intersections. We also present the inclusion matrix, which contains the pairwise inclusion relationships between contours, and leverage it to accelerate the recomputation of ID. Secondly, extending beyond the single distribution assumption, we present the Relative Depth (ReD), a generalization of contour depth for ensembles with multiple modes. Building upon the linear-time eID, we introduce CDclust, a clustering algorithm that untangles ensemble modes of variation by optimizing ReD. Synthetic and real datasets from medical image segmentation and meteorological forecasting showcase the speed advantages, illustrate the use case of progressive depth computation and enable non-parametric multimodal analysis. To promote research and adoption, we offer the contour-depth Python package.
Visual Analytics for Fine-grained Text Classification Models and Datasets
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Battogtokh, Munkhtulga; Xing, Yiwen; Davidescu, Cosmin; Abdul-Rahman, Alfie; Luck, Michael; Borgo, Rita; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
In natural language processing (NLP), text classification tasks are increasingly fine-grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse-grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design-and-evaluation process to characterize and tackle the growing requirements in their workflow of developing fine-grained text classification models. The result of this collaboration is the development of SemLa, a novel Visual Analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine-grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation.
Beyond ExaBricks: GPU Volume Path Tracing of AMR Data
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zellmann, Stefan; Wu, Qi; Sahistan, Alper; Ma, Kwan-Liu; Wald, Ingo; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for HPC, and thus also for scientific visualization. AMR data is usually cell centric (which imposes numerous challenges), complex, and generally hard to render. Recent work on GPU-accelerated AMR rendering has made much progress towards real-time volume and isosurface rendering of such data, but so far this work has focused exclusively on ray marching, with simple lighting models and without scattering events or global illumination. True high-quality rendering requires a modified approach that is able to trace arbitrary incoherent paths; but this may not be a perfect fit for the types of data structures recently developed for ray marching. In this paper, we describe a novel approach to high-quality path tracing of complex AMR data, with a specific focus on analyzing and comparing different data structures and algorithms to achieve this goal.
AutoVizuA11y: A Tool to Automate Screen Reader Accessibility in Charts
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Duarte, Diogo; Costa, Rita; Bizarro, Pedro; Duarte, Carlos; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Charts remain widely inaccessible on the web for users of assistive technologies like screen readers. This is, in part, due to data visualization experts still lacking the experience, knowledge, and time to consistently implement accessible charts. As a result, screen reader users are prevented from accessing information and are forced to resort to tabular alternatives (if available), limiting the insights that they can gather. We worked with both groups to develop AutoVizuA11y, a tool that automates the addition of accessible features to web-based charts. It generates human-like descriptions of the data using a large language model, calculates statistical insights from the data, and provides keyboard navigation between multiple charts and underlying elements. Fifteen screen reader users interacted with charts made accessible with AutoVizuA11y in a usability test, thirteen of which praised the tool for its intuitive design, short learning curve, and rich information. On average, they took 66 seconds to complete each of the eight analytical tasks presented and achieved a success rate of 89%. Through a SUS questionnaire, the participants gave AutoVizuA11y an ''Excellent'' score-83.5/100 points. We also gathered feedback from two data visualization experts who used the tool. They praised the tool availability, ease of use and functionalities, and provided feedback to add AutoVizuA11y support for other technologies in the future.
Sparse q-ball imaging towards efficient visual exploration of HARDI data
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lei, Danhua; Miandji, Ehsan; Unger, Jonas; Hotz, Ingrid; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Diffusion-weighted magnetic resonance imaging (D-MRI) is a technique to measure the diffusion of water, in biological tissues. It is used to detect microscopic patterns, such as neural fibers in the living human brain, with many medical and neuroscience applications e.g. for fiber tracking. In this paper, we consider High-Angular Resolution Diffusion Imaging (HARDI) which provides one of the richest representations of water diffusion. It records the movement of water molecules by measuring diffusion under 64 or more directions. A key challenge is that it generates high-dimensional, large, and complex datasets. In our work, we develop a novel representation that exploits the inherent sparsity of the HARDI signal by approximating it as a linear sum of basic atoms in an overcomplete data-driven dictionary using only a sparse set of coefficients. We show that this approach can be efficiently integrated into the standard q-ball imaging pipeline to compute the diffusion orientation distribution function (ODF). Sparse representations have the potential to reduce the size of the data while also giving some insight into the data. To explore the results, we provide a visualization of the atoms of the dictionary and their frequency in the data to highlight the basic characteristics of the data. We present our proposed pipeline and demonstrate its performance on 5 HARDI datasets.
ChoreoVis: Planning and Assessing Formations in Dance Choreographies
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Beck, Samuel; Doerr, Nina; Kurzhals, Kuno; Riedlinger, Alexander; Schmierer, Fabian; Sedlmair, Michael; Koch, Steffen; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real-world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.
Interactive Optimization for Cartographic Aggregation of Building Features
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Takahashi, Shigeo; Kokubun, Ryo; Nishimura, Satoshi; Misue, Kazuo; Arikawa, Masatoshi; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Aggregation, as an operation of cartographic generalization, provides an effective means of abstracting the configuration of building features by combining them according to the scale reduction of the 2D map. Automating this design process effectively helps professional cartographers design both paper and digital maps, but finding the best aggregation result from the numerous combinations of building features has been a challenge. This paper presents a novel approach to assist cartographers in interactively designing the aggregation of building features in scale-aware map visualization. Our contribution is to provide an appropriate set of candidates for the cartographer to choose from among a limited number of possible combinations of building features. This is achieved by collecting locally optimal solutions that emerge in the course of aggregation operations, formulated as a label cost optimization problem. Users can also explore better aggregation results by interactively adjusting the design parameters to update the set of possible combinations, along with an operator to force the combination of manually selected building features. Each cluster of aggregated building features is tightly enclosed by a concave hull, which is later adaptively simplified to abstract its boundary shapes. Experimental design examples and evaluations by expert cartographers demonstrate the feasibility of the proposed approach to interactive aggregation.
ProtEGOnist: Visual Analysis of Interactions in Small World Networks Using Ego-graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Brich, Nicolas; Harbig, Theresa A.; Witte Paz, Mathias; Nieselt, Kay; Krone, Michael; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Visualizing small-world networks such as protein-protein interaction networks or social networks often leads to visual clutter and limited interpretability. To overcome these problems, we present ProtEGOnist, a visualization approach designed to explore small-world networks. ProtEGOnist visualizes networks using ego-graphs that represent local neighborhoods. Egographs are visualized in an aggregated state as a glyph where the size encodes the size of the neighborhood and in a detailed version where the original network nodes can be explored. The ego-graphs are arranged in an ego-graph network, where edges encode similarity using the Jaccard index. Our design aims to reduce visual complexity and clutter while enabling detailed exploration and facilitating the discovery of meaningful patterns. To achieve this, our approach offers a network overview using ego-graphs, a radar chart for a one-to-many ego-graph comparison and meta-data integration, and detailed ego-graph subnetworks for interactive exploration. We demonstrate the applicability of our approach on a co-author network and two different protein-protein interaction networks. A web-based prototype of ProtEGOnist can be accessed online at https://protegonist-tuevis.cs.uni-tuebingen.de/.
RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lv, Cheng; Zhang, Huijie; Lin, Yiming; Dong, Jialu; Tian, Liang; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Analyzing the preference of route choice not only facilitates the understanding of individuals' decision-making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed-loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio-temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status.
Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Ha, Sunwoo; Monadjemi, Shayan; Ottley, Alvitta; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a more tedious scenario for the analyst. Our results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite the AI's lower suggestion accuracy. Moreover, the levels of transparency tested in this study did not significantly affect suggestion usage or subjective trust ratings of the participants. Additionally, we observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points. We discuss these findings and the implications of this research for improving the design and effectiveness of AI-guided VA tools.

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