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2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)最新文献

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Beyond Trust Building — Calibrating Trust in Visual Analytics 超越信任建立——在视觉分析中校准信任
2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2020-10-01 DOI: 10.1109/TREX51495.2020.00006
Wenkai Han, Hans-Jörg Schulz
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引用次数: 15
[Title page i] [标题页i]
2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2020-10-01 DOI: 10.1109/trex51495.2020.00001
{"title":"[Title page i]","authors":"","doi":"10.1109/trex51495.2020.00001","DOIUrl":"https://doi.org/10.1109/trex51495.2020.00001","url":null,"abstract":"","PeriodicalId":314096,"journal":{"name":"2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133344179","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
[Title page iii] [标题页iii]
2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2020-10-01 DOI: 10.1109/trex51495.2020.00002
{"title":"[Title page iii]","authors":"","doi":"10.1109/trex51495.2020.00002","DOIUrl":"https://doi.org/10.1109/trex51495.2020.00002","url":null,"abstract":"","PeriodicalId":314096,"journal":{"name":"2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116109203","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
Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling 面向数据驱动能源建模的增强信任可视化分析
2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2020-10-01 DOI: 10.1109/TREX51495.2020.00007
Akshith Reddy Kandakatla, V. Chandan, Soumya Kundu, Indrasis Chakraborty, Kristin A. Cook, Aritra Dasgupta
{"title":"Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling","authors":"Akshith Reddy Kandakatla, V. Chandan, Soumya Kundu, Indrasis Chakraborty, Kristin A. Cook, Aritra Dasgupta","doi":"10.1109/TREX51495.2020.00007","DOIUrl":"https://doi.org/10.1109/TREX51495.2020.00007","url":null,"abstract":"The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytical goals in this domain.","PeriodicalId":314096,"journal":{"name":"2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121192146","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: 1
Measure Utility, Gain Trust: Practical Advice for XAI Researchers 衡量效用，获得信任:对XAI研究人员的实用建议
2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2020-09-27 DOI: 10.1109/TREX51495.2020.00005
Brittany Davis, M. Glenski, William I. N. Sealy, Dustin L. Arendt
{"title":"Measure Utility, Gain Trust: Practical Advice for XAI Researchers","authors":"Brittany Davis, M. Glenski, William I. N. Sealy, Dustin L. Arendt","doi":"10.1109/TREX51495.2020.00005","DOIUrl":"https://doi.org/10.1109/TREX51495.2020.00005","url":null,"abstract":"Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.","PeriodicalId":314096,"journal":{"name":"2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125068277","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: 22
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