Visus

Santos, Ana Sílvia Pereira; Castelo, Sonia; Felix, Cristian; Ono, Jorge Piazentin; Yu, Bowen; Hong, Sung‐Soo; Silva, Cláudio T.; Bertini, Enrico; Freire, Juliana

doi:10.1145/3328519.3329134

Cited by 19 publications

(6 citation statements)

References 11 publications

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“…While social sensemaking and annotation have inspired numerous applications in CSCW, there have been relatively a scarce of approaches that leverage AI in facilitating novices' sensemaking process, e.g., [30,34,35,58,76] in troubleshooting. Rather, many focused on improving 3D printing computational pipelines, such as real-time failure detection systems with video cameras.…”

Section: Related Workmentioning

confidence: 99%

3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration Design

Kwon,

Sun,

Gao

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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The widespread consumer-grade 3D printers and learning resources online enable novices to self-train in remote settings. While troubleshooting plays an essential part of 3D printing, the process remains challenging for many remote novices even with the help of well-developed online sources, such as online troubleshooting archives and online community help. We conducted a formative study with 76 active 3D printing users to learn how remote novices leverage online resources in troubleshooting and their challenges. We found that remote novices cannot fully utilize online resources. For example, the online archives statically provide general information, making it hard to search and relate their unique cases with existing descriptions. Online communities can potentially ease their struggles by providing more targeted suggestions, but a helper who can provide custom help is rather scarce, making it hard to obtain timely assistance. We propose 3DPFIX, an interactive 3D troubleshooting system powered by the pipeline to facilitate Human-AI Collaboration, designed to improve novices' 3D printing experiences and thus help them easily accumulate their domain knowledge. We built 3DPFIX that supports automated diagnosis and solution-seeking. 3DPFIX was built upon shared dialogues about failure cases from Q&A discourses accumulated in online communities. We leverage social annotations (i.e., comments) to build an annotated failure image dataset for AI classifiers and extract a solution pool. Our summative study revealed that using 3DPFIX helped participants spend significantly less effort in diagnosing failures and finding a more accurate solution than relying on their common practice. We also found that 3DPFIX users learn about 3D printing domain-specific knowledge. We discuss the implications of leveraging community-driven data in developing future Human-AI Collaboration designs.

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Section: Related Workmentioning

confidence: 99%

3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration Design

Kwon,

Sun,

Gao

et al. 2024

Proc. ACM Hum.-Comput. Interact.

Self Cite

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“…AutoVizAI [104] similarly explores the narrow scope of model configurations but uses conditional parallel coordinate plots to visualize the model generation across possible configurations. Lastly, Visus [83] targets how domain experts specifically can tackle model building using AutoML.…”

Section: Automl Visualization Systemsmentioning

confidence: 99%

Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work

Rogers

Crisan

2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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“…As the goals of ML models become more contextualized and domain-specific [3,4,49,62], however, many annotation task types require advanced knowledge that is beyond that of beginner annotators. Examples of such domain-specific problems include damage assessment [14,33], risk analysis [51], information filtering for disaster management [44], medical image reading [41], and many more [29].…”

Section: Introductionmentioning

confidence: 99%

Closing the Knowledge Gap in Designing Data Annotation Interfaces for AI-powered Disaster Management Analytic Systems

Ara,

Salemi,

Hong

et al. 2024

Proceedings of the 29th International Conference on Intelligent User Interfaces

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Data annotation interfaces predominantly leverage ground truth labels to guide annotators toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain-specific professional tasks, it has become increasingly important to help beginning annotators identify how their early-stage knowledge can lead to inaccurate answers, which in turn, helps to ensure quality annotations at scale. To investigate this issue, we conducted a formative study involving eight individuals from the field of disaster management, each possessing varying levels of expertise. The goal was to understand the prevalent factors contributing to disagreements among annotators when classifying Twitter messages related to disasters and to analyze their respective responses. Our analysis identified two primary causes of disagreement between expert and beginner annotators: 1) a lack of contextual knowledge or uncertainty about the situation, and 2) the absence of visual or supplementary cues. Based on these findings, we designed a Context interface, which generates aids that help beginners identify potential mistakes and provide the hidden context of the presented tweet. The summative study compares Context design with two widely used designs in data annotation UI, Highlight and Reasoningbased interfaces. We found significant differences between these designs in terms of attitudinal and behavioral data. We conclude with implications for designing future interfaces aiming at closing the knowledge gap among annotators. CCS CONCEPTS• Human-centered computing → Empirical studies in interaction design; User interface design.

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Visus

Cited by 19 publications

References 11 publications

3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration Design

3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration Design

Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work

Closing the Knowledge Gap in Designing Data Annotation Interfaces for AI-powered Disaster Management Analytic Systems

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