2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)最新文献

Kicking Analysts Out of the Meeting Room: Supporting Future Data-driven Decision Making with Intelligent Interactive Visualization Systems 把分析师赶出会议室:用智能交互式可视化系统支持未来数据驱动的决策制定

2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2022-10-01 DOI: 10.1109/TREX57753.2022.00007

Yi Han

引用次数: 1

Welcome from the Workshop Organizers TREX 2022 欢迎来自研讨会组织者TREX 2022

2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2022-10-01 DOI: 10.1109/trex57753.2022.00016

引用次数: 0

How Do Algorithmic Fairness Metrics Align with Human Judgement? A Mixed-Initiative System for Contextualized Fairness Assessment 算法公平指标如何与人类判断保持一致?情境化公平评估的混合主动系统

2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2022-10-01 DOI: 10.1109/TREX57753.2022.00005

Rareş Constantin, Moritz Dück, Anton Alexandrov, Patrik Matošević, Daphna Keidar, Mennatallah El-Assady

{"title":"How Do Algorithmic Fairness Metrics Align with Human Judgement? A Mixed-Initiative System for Contextualized Fairness Assessment","authors":"Rareş Constantin, Moritz Dück, Anton Alexandrov, Patrik Matošević, Daphna Keidar, Mennatallah El-Assady","doi":"10.1109/TREX57753.2022.00005","DOIUrl":"https://doi.org/10.1109/TREX57753.2022.00005","url":null,"abstract":"Fairness evaluation presents a challenging problem in machine learning, and is usually restricted to the exploration of various metrics that attempt to quantify algorithmic fairness. However, due to cultural and perceptual biases, such metrics are often not powerful enough to accurately capture what people perceive as fair or unfair. To close the gap between human judgement and automated fairness evaluation, we develop a mixed-initiative system named FairAlign, where laypeople assess the fairness of different classification models by analyzing expressive and interactive visualizations of data. Using the aggregated qualitative feedback, data scientists and machine learning experts can examine the similarities and the differences between predefined fairness metrics and human judgement in a contextualized setting. To validate the utility of our system, we conducted a small study on a socially relevant classification task, where six people were asked to assess the fairness of multiple prediction models using the provided visualizations. The results show that our platform is able to give valuable guidance for model evaluation in case of otherwise contradicting and indecisive metrics for algorithmic fairness.","PeriodicalId":150871,"journal":{"name":"2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134206329","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

Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy 可信的视觉分析在临床步态分析:脑瘫患者的案例研究

2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) Pub Date : 2022-08-10 DOI: 10.1109/TREX57753.2022.00006

A. Rind, D. Slijepcevic, M. Zeppelzauer, F. Unglaube, A. Kranzl, B. Horsak

{"title":"Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy","authors":"A. Rind, D. Slijepcevic, M. Zeppelzauer, F. Unglaube, A. Kranzl, B. Horsak","doi":"10.1109/TREX57753.2022.00006","DOIUrl":"https://doi.org/10.1109/TREX57753.2022.00006","url":null,"abstract":"Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine learning approaches yield promising results. However, due to their black-box nature, such approaches are often mistrusted by clinicians. We propose gaitXplorer, a visual analytics approach for the classification of CP-related gait patterns that integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications. Regions of high relevance for classification are highlighted in the interactive visual interface. The approach is evaluated in a case study with two clinical gait experts. They inspected the explanations for a sample of eight patients using the visual interface and expressed which relevance scores they found trustworthy and which they found suspicious. Overall, the clinicians gave positive feedback on the approach as it allowed them a better understanding of which regions in the data were relevant for the classification.","PeriodicalId":150871,"journal":{"name":"2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129463058","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