Engaging with Data

会議の名前
CHI 2025
Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction
要旨

Dimensionality reduction (DR) techniques are essential for visually analyzing high-dimensional data. However, visual analytics using DR often face unreliability, stemming from factors such as inherent distortions in DR projections. This unreliability can lead to analytic insights that misrepresent the underlying data, potentially resulting in misguided decisions. To tackle these reliability challenges, we review 133 papers that address the unreliability of visual analytics using DR. Through this review, we contribute (1) a workflow model that describes the interaction between analysts and machines in visual analytics using DR, and (2) a taxonomy that identifies where and why reliability issues arise within the workflow, along with existing solutions for addressing them. Our review reveals ongoing challenges in the field, whose significance and urgency are validated by five expert researchers. This review also finds that the current research landscape is skewed toward developing new DR techniques rather than their interpretation or evaluation, where we discuss how the HCI community can contribute to broadening this focus.

著者
Hyeon Jeon
Seoul National University, Seoul, Korea, Republic of
Hyunwook Lee
Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of
Yun-Hsin Kuo
University of California, Davis, Davis, California, United States
Taehyun Yang
Seoul National University, Seoul, Korea, Republic of
Daniel Archambault
Newcastle University, Newcastle, United Kingdom
Sungahn Ko
UNIST, Ulsan, Korea, Republic of
Takanori Fujiwara
Linköping University, Norrköping, Sweden
Kwan-Liu Ma
University of California at Davis, Davis, California, United States
Jinwook Seo
Seoul National University, Seoul, Korea, Republic of
DOI

10.1145/3706598.3713551

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713551

動画
Disentangling the Power Dynamics in Participatory Data Physicalisation
要旨

Participatory data physicalisation (PDP) is recognised for its potential to support data-driven decisions among stakeholders who collaboratively construct physical elements into commonly insightful visualisations. Like all participatory processes, PDP is however influenced by underlying power dynamics that might lead to issues regarding extractive participation, marginalisation, or exclusion, among others. We first identified the decisions behind these power dynamics by developing an ontology that synthesises critical theoretical insights from both visualisation and participatory design research, which were then systematically applied unto a representative corpus of 23 PDP artefacts. By revealing how shared decisions are guided by different agendas, this paper presents three contributions: 1) a cross-disciplinary ontology that facilitates the systematic analysis of existing and novel PDP artefacts and processes; which leads to 2) six PDP agendas that reflect the key power dynamics in current PDP practice, revealing the diversity of orientations towards stakeholder participation in PDP practice; and 3) a set of critical considerations that should guide how power dynamics can be balanced, such as by reflecting on how issues are represented, data is contextualised, participants express their meanings, and how participants can dissent with flexible artefact construction. Consequently, this study advances a feminist research agenda by guiding researchers and practitioners in openly reflecting on and sharing responsibilities in data physicalisation and participatory data visualisation.

受賞
Honorable Mention
著者
Silvia Cazacu
KU Leuven, Leuven, Belgium
Georgia Panagiotidou
King's College London, London, United Kingdom
Therese Steenberghen
Division of Geography and Tourism, Leuven, Belgium
Andrew Vande Moere
KU Leuven, Leuven, Belgium
DOI

10.1145/3706598.3713703

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713703

動画
Access Denied: Meaningful Data Access for Quantitative Algorithm Audits
要旨

Independent algorithm audits hold the promise of bringing accountability to automated decision-making. However, third-party audits are often hindered by access restrictions, forcing auditors to rely on limited, low-quality data. To study how these limitations impact research integrity, we conduct audit simulations on two realistic case studies for recidivism and healthcare coverage prediction. We examine the accuracy of estimating group parity metrics across three levels of access: (a) aggregated statistics, (b) individual-level data with model outputs, and (c) individual-level data without model outputs. Despite selecting one of the simplest tasks for algorithmic auditing, we find that data minimization and anonymization practices can strongly increase error rates on individual-level data, leading to unreliable assessments. We discuss implications for independent auditors, as well as potential avenues for HCI researchers and regulators to improve data access and enable both reliable and holistic evaluations.

受賞
Honorable Mention
著者
Juliette Zaccour
University of Oxford, Oxford, United Kingdom
Reuben Binns
University of Oxford, Oxford, United Kingdom
Luc Rocher
University of Oxford, Oxford, United Kingdom
DOI

10.1145/3706598.3713963

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713963

動画
Management, Cooperation, and Sustainability: Unpacking the Data Practices of Housing Cooperatives
要旨

Despite significant work in HCI on understanding the role of data tools for non-profits and grassroots communities, there has been limited focus on cooperatives. This paper examines the role of financial, social, and building upkeep data in a non-profit cooperative housing organization in Toronto, Canada (alias named NXI). Through a 16-month-long ethnographic study, including 24 interviews, we investigate the role of and tensions in data practices related to NXI’s daily maintenance and operations, cooperation, and sustainability. We find that NXI’s current data practices are functional and meaningful—sometimes requiring team workarounds—in the short term. However, various tensions and deficiencies in data practices hamper NXI’s sustainability. By contextualizing the temporal affordances of data, we propose design implications for data tools to align effectively with the practices of cooperatives and enhance organizational sustainability. Finally, we discuss how data designers and researchers, organizations or grassroots communities, and financial technology designers can benefit from our work, especially with regard to the maintenance and sustainability of small-scale organizations.

著者
Priyanka Verma
University of Toronto, Toronto, Ontario, Canada
Mohammad Rashidujjaman Rifat
University of Toronto, Toronto, Ontario, Canada
Samar Sabie
University of Toronto, Toronto, Ontario, Canada
DOI

10.1145/3706598.3713121

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713121

動画
Dango: A Mixed-Initiative Data Wrangling System using Large Language Model
要旨

Data wrangling is a time-consuming and challenging task in the early stages of a data science pipeline. However, existing tools often fail to effectively interpret user intent. We propose Dango, a mixed-initiative multi-agent system that helps users generate data wrangling scripts. Compared to existing tools, Dango enhances user communication of intent by: (1) allowing users to demonstrate on multiple tables and use natural language prompts in a conversation interface, (2) enabling users to clarify their intent by answering LLM-posed multiple-choice clarification questions, and (3) providing multiple forms of feedback such as step-by-step NL explanations and data provenance to help users evaluate the data wrangling scripts. In a within-subjects, think-aloud study (n=38), the results show that Dango's features can significantly improve intent clarification, accuracy, and efficiency in data wrangling tasks.

著者
Wei-Hao Chen
Purdue University, West Lafayette, Indiana, United States
Weixi Tong
Huazhong University of Science and Technology, Wuhan, China
Amanda Case
University of Iowa, Iowa City, Iowa, United States
Tianyi Zhang
Purdue University, West Lafayette, Indiana, United States
DOI

10.1145/3706598.3714135

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714135

動画
RidgeBuilder: Interactive Authoring of Expressive Ridgeline Plots
要旨

Ridgeline plots are frequently employed to visualize the evolution or distributions of multiple series with a pile of overlapping line, area, or bar charts, highlighting the peak patterns. While traditionally viewed as small multiple visualizations, their ridge-like patterns have increasingly attracted graphic designers to create appealing customized ridgeline plots. However, many tools only support creating basic ridgeline plots and overlook their diverse layouts and styles. This paper introduces a comprehensive design space for ridgeline plots, focusing on their varied layouts and expressive styles. We present RidgeBuilder, an intuitive tool for creating expressive ridgeline plots with customizable layouts and styles. In particular, we summarize three goals for refining the layout of ridgeline plots and propose an optimization method. We assess RidgeBuilder's usability and usefulness through a reproduction study and evaluate the layout optimization algorithm through anonymized questionnaires. The effectiveness is demonstrated with a gallery of ridgeline plots created by RidgeBuilder.

著者
Shuhan Liu
State Key Lab of CAD & CG, Zhejiang University, Hangzhou, Zhejiang, China
Yangtian Liu
Zhejiang University, Ningbo, Zhejiang, China
Junxin Li
Zhejiang University, Hangzhou, Zhejiang, China
Yanwei Huang
Zhejiang University, Hangzhou, Zhejiang, China
Yue Shangguan
University of Texas at Austin, Austin, Texas, United States
Zikun Deng
South China University of Technology, Guangzhou, Guangdong, China
Di Weng
Zhejiang University, Ningbo, Zhejiang, China
Yingcai Wu
Zhejiang University, Hangzhou, Zhejiang, China
DOI

10.1145/3706598.3714209

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714209

動画
StructVizor: Interactive Profiling of Semi-Structured Textual Data
要旨

Data profiling plays a critical role in understanding the structure of complex datasets and supporting numerous downstream tasks, such as social media analytics and financial fraud detection. While existing research predominantly focuses on structured data formats, a substantial portion of semi-structured textual data still requires ad-hoc and arduous manual profiling to extract and comprehend its internal structures. In this work, we propose StructVizor, an interactive profiling system that facilitates sensemaking and transformation of semi-structured textual data. Our tool mainly addresses two challenges: a) extracting and visualizing the diverse structural patterns within data, such as how information is organized or related, and b) enabling users to efficiently perform various wrangling operations on textual data. Through automatic data parsing and structure mining, StructVizor enables visual analytics of structural patterns, while incorporating novel interactions to enable profile-based data wrangling. A comparative user study involving 12 participants demonstrates the system's usability and its effectiveness in supporting exploratory data analysis and transformation tasks.

著者
Yanwei Huang
Zhejiang University, Hangzhou, Zhejiang, China
Yan Miao
Zhejiang University, Hangzhou, Zhejiang, China
Di Weng
Zhejiang University, Ningbo, Zhejiang, China
Adam Perer
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Yingcai Wu
Zhejiang University, Hangzhou, Zhejiang, China
DOI

10.1145/3706598.3713484

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713484

動画