2022 IEEE Visualization and Visual Analytics (VIS)最新文献_第4页

Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases 可视化审计师:用于检测和总结模型偏差的交互式可视化

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-06-25 DOI: 10.1109/VIS54862.2022.00018

David Munechika, Zijie J. Wang, Jack Reidy, Josh Rubin, Krishna Gade, K. Kenthapadi, Duen Horng Chau

{"title":"Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases","authors":"David Munechika, Zijie J. Wang, Jack Reidy, Josh Rubin, Krishna Gade, K. Kenthapadi, Duen Horng Chau","doi":"10.1109/VIS54862.2022.00018","DOIUrl":"https://doi.org/10.1109/VIS54862.2022.00018","url":null,"abstract":"As machine learning (ML) systems become increasingly widespread, it is necessary to audit these systems for biases prior to their de-ployment. Recent research has developed algorithms for effectively identifying intersectional bias in the form of interpretable, underper-forming subsets (or slices) of the data. However, these solutions and their insights are limited without a tool for visually understanding and interacting with the results of these algorithms. We propose Visual Auditor, an interactive visualization tool for auditing and summarizing model biases. Visual Auditor assists model validation by providing an interpretable overview of intersectional bias (bias that is present when examining populations defined by multiple features), details about relationships between problematic data slices, and a comparison between underperforming and overper-forming data slices in a model. Our open-source tool runs directly in both computational notebooks and web browsers, making model auditing accessible and easily integrated into current ML development workflows. An observational user study in collaboration with domain experts at Fiddler AI highlights that our tool can help ML practitioners identify and understand model biases.","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125371166","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: 9

Plotly-Resampler: Effective Visual Analytics for Large Time Series plot - resampler:大时间序列的有效视觉分析

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-06-17 DOI: 10.1109/VIS54862.2022.00013

Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost, S. Hoecke

{"title":"Plotly-Resampler: Effective Visual Analytics for Large Time Series","authors":"Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost, S. Hoecke","doi":"10.1109/VIS54862.2022.00013","DOIUrl":"https://doi.org/10.1109/VIS54862.2022.00013","url":null,"abstract":"Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four requirements have to be met; a tool should be (1) interactive, (2) scalable to millions of data points, (3) integrable in conventional data science environments, and (4) highly configurable. We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization. As a means to facilitate these requirements, we created Plotly-Resampler, an open source Python library. Plotly-Resampler is an add-on for Plotly's Python bindings, enhancing line chart scalability on top of an interactive toolkit by aggregating the underlying data depending on the current graph view. Plotly-Resampler is built to be snappy, as the reactivity of a tool qualitatively affects how analysts visually explore and analyze data. A benchmark task highlights how our toolkit scales better than alternatives in terms of number of samples and time series. Additionally, Plotly-Resampler's flexible data aggregation functionality paves the path towards researching novel aggregation techniques. Plotly-Resampler's integrability, together with its configurability, convenience, and high scalability, allows to effectively analyze high-frequency data in your day-to-day Python environment.","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124904400","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: 10

Uniform Manifold Approximation with Two-phase Optimization 统一流形逼近与两阶段优化

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-05-01 DOI: 10.1109/VIS54862.2022.00025

Hyeon Jeon, Hyung-Kwon Ko, S. Lee, Jaemin Jo, Jinwook Seo

引用次数: 6

Toward Systematic Considerations of Missingness in Visual Analytics 对视觉分析中缺失的系统考虑

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2021-08-10 DOI: 10.1109/VIS54862.2022.00031

Maoyuan Sun, Yue Ma, Yuanxin Wang, Tianyi Li, Jian Zhao, Yujun Liu, Ping-Shou Zhong

{"title":"Toward Systematic Considerations of Missingness in Visual Analytics","authors":"Maoyuan Sun, Yue Ma, Yuanxin Wang, Tianyi Li, Jian Zhao, Yujun Liu, Ping-Shou Zhong","doi":"10.1109/VIS54862.2022.00031","DOIUrl":"https://doi.org/10.1109/VIS54862.2022.00031","url":null,"abstract":"Data-driven decision making has been a common task in today's big data era, from simple choices such as finding a fast way to drive home, to complex decisions on medical treatment. It is often supported by visual analytics. For various reasons (e.g., system failure, interrupted network, intentional information hiding, or bias), visual analytics for sensemaking of data involves missingness (e.g., data loss and incomplete analysis), which impacts human decisions. For example, missing data can cost a business millions of dollars, and failing to recognize key evidence can put an innocent person in jail. Being aware of missingness is critical to avoid such catastrophes. To fulfill this, as an initial step, we consider missingness in visual analytics from two aspects: data-centric and human-centric. The former emphasizes missingness in three data-related categories: data composition, data relationship, and data usage. The latter focuses on the human-perceived missingness at three levels: observed-level, inferred-level, and ignored-level. Based on them, we discuss possible roles of visualizations for handling missingness, and conclude our discussion with future research opportunities.","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128814141","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

Exploring D3 Implementation Challenges on Stack Overflow 探索D3在堆栈溢出上的实现挑战

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2021-08-04 DOI: 10.1109/VIS54862.2022.00009

L. Battle, Danni Feng, Kelli Webber

引用次数: 8

Guided Data Discovery in Interactive Visualizations via Active Search 通过主动搜索在交互式可视化中引导数据发现

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2020-10-16 DOI: 10.1109/VIS54862.2022.00023

S. Monadjemi, Sunwoo Ha, Quan Nguyen, Henry Chai, R. Garnett, Alvitta Ottley

引用次数: 1