2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)最新文献

Title Page iii 第三页标题

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/trex53765.2021.00002

引用次数: 0

Should I Follow this Model? The Effect of Uncertainty Visualization on the Acceptance of Time Series Forecasts 我应该遵循这种模式吗?不确定性可视化对时间序列预测可接受性的影响

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/TREX53765.2021.00009

Dirk Leffrang, Oliver Müller

{"title":"Should I Follow this Model? The Effect of Uncertainty Visualization on the Acceptance of Time Series Forecasts","authors":"Dirk Leffrang, Oliver Müller","doi":"10.1109/TREX53765.2021.00009","DOIUrl":"https://doi.org/10.1109/TREX53765.2021.00009","url":null,"abstract":"Time series forecasts are ubiquitous, ranging from daily weather forecasts to projections of pandemics such as COVID-19. Communicating the uncertainty associated with such forecasts is important, because it may affect users’ trust in a forecasting model and, in turn, the decisions made based on the model. Although there exists a growing body of research on visualizing uncertainty in general, the important case of visualizing prediction uncertainty in time series forecasting is under-researched. Against this background, we investigated how different visualizations of predictive uncertainty affect the extent to which people follow predictions of a time series forecasting model. More specifically, we conducted an online experiment on forecasting occupied hospital beds due to the COVID-19 pandemic, measuring the influence of uncertainty visualization of algorithmic predictions on participants’ own predictions. In contrast to prior studies, our empirical results suggest that more salient visualizations of uncertainty lead to decreased willingness to follow algorithmic forecasts.","PeriodicalId":345585,"journal":{"name":"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114530921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking 超越视觉分析:人工智能驱动的数据语义的人机合作

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/TREX53765.2021.00012

John E. Wenskovitch, C. Fallon, Kate Miller, Aritra Dasgupta

{"title":"Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking","authors":"John E. Wenskovitch, C. Fallon, Kate Miller, Aritra Dasgupta","doi":"10.1109/TREX53765.2021.00012","DOIUrl":"https://doi.org/10.1109/TREX53765.2021.00012","url":null,"abstract":"Detect the expected, discover the unexpected was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sense-making process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.","PeriodicalId":345585,"journal":{"name":"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134369004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Welcome from the TREX 2021 Organizers 欢迎来自TREX 2021的组织者

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/trex53765.2021.00005

引用次数: 0

How to deal with Uncertainty in Machine Learning for Medical Imaging? 如何处理医学成像机器学习中的不确定性?

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/TREX53765.2021.00014

C. Gillmann, D. Saur, G. Scheuermann

引用次数: 7

The Enhanced Security in Process System - Evaluating Knowledge Assistance 过程系统中增强的安全性——评估知识援助

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/TREX53765.2021.00006

A. Lohfink, V. M. Memmesheimer, Frederike Gartzky, C. Garth

{"title":"The Enhanced Security in Process System - Evaluating Knowledge Assistance","authors":"A. Lohfink, V. M. Memmesheimer, Frederike Gartzky, C. Garth","doi":"10.1109/TREX53765.2021.00006","DOIUrl":"https://doi.org/10.1109/TREX53765.2021.00006","url":null,"abstract":"We present evaluation results of our enhancements to the Security in Process System [14] developed by Lohfink et al. to support triage analysis in operational technology networks. To ensure fast and appropriate reactions to anomalies in device readings, this system communicates anomaly detection results and device readings to incorporate human expertise and experience. It exploits periodical behavior in the data combining spiral plots with results from anomaly detection. To support decisions, increase trust, and support cooperation in the system we enhanced it to be knowledge-assisted. A central knowledge base allows sharing knowledge between users and support during analysis. It consists of an ontology describing incidents, and a data base holding collections of exemplary sensor readings with annotations and visualization parameters. Related knowledge is proposed automatically and incorporated directly in the visualization to provide assistance that is closely coupled to the application, without additional hurdles. This integration is designed aiming on additional support for correct and fast detection of anomalies in the visualized device readings. We evaluate our enhancements to the Security in Process System in terms of effectiveness, efficiency, user satisfaction, and cognitive load with a detailed user study. Comparing the original and enhanced system, we are able to draw conclusions as to how our design narrows the knowledge gap between professional analysts and laymen. Furthermore, we present and discuss the results and impact on our future research.","PeriodicalId":345585,"journal":{"name":"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132748206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Case Study of Using Analytic Provenance to Reconstruct User Trust in a Guided Visual Analytics System 利用分析来源重建导引式视觉分析系统中的用户信任案例研究

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/TREX53765.2021.00013

N. Boukhelifa, E. Lutton, A. Bezerianos

{"title":"A Case Study of Using Analytic Provenance to Reconstruct User Trust in a Guided Visual Analytics System","authors":"N. Boukhelifa, E. Lutton, A. Bezerianos","doi":"10.1109/TREX53765.2021.00013","DOIUrl":"https://doi.org/10.1109/TREX53765.2021.00013","url":null,"abstract":"In this paper, we demonstrate how analytic provenance can be exploited to re-construct user trust in a guided Visual Analytics (VA) system, and suggest that interaction log data analysis can be a valuable tool for on-line trust monitoring. Our approach explores objective trust measures that can be continuously tracked and updated during the exploration, and reflect both the confidence of the user in system suggestions, and the uncertainty of the system with regards to user goals. We argue that this approach is more suitable for guided VA systems such as ours, where user strategies, goals and even trust can evolve over time, in reaction to new system feedback and insights from the exploration. Through the analysis of log data from a past user study with twelve participants performing a guided visual analysis task, we found that the stability of user exploration strategies is a promising factor to study trust. However, indirect metrics based on provenance, such as user evaluation counts and disagreement rates, are alone not sufficient to study trust reliably in guided VA. We conclude with open challenges and opportunities for exploiting analytic provenance to support trust monitoring in guided VA systems.","PeriodicalId":345585,"journal":{"name":"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125191477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Forecasting, Knowledge, and Visual Analytics 评估预测、知识和可视化分析

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/TREX53765.2021.00011

Yafeng Lu, Michael Steptoe, Verica Buchanan, Nancy J. Cooke, Ross Maciejewski

引用次数: 1

Making and Trusting Decisions in Visual Analytics 在可视化分析中做出和信任决策

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-10-01 DOI: 10.1109/TREX53765.2021.00008

Wenkai Han

引用次数: 0

Time Series Model Attribution Visualizations as Explanations 时间序列模型归因可视化作为解释

2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2021-09-27 DOI: 10.1109/TREX53765.2021.00010

U. Schlegel, D. Keim

引用次数: 10