Adaptive Contextualization Methods for Combating Selection Bias During High-Dimensional Visualization. Gotz, D., Sun, S., Cao, N., Kundu, R., & Meyer, A. ACM Trans. Interact. Intell. Syst., 7(4):17:1–17:23, November, 2017. ![Adaptive Contextualization Methods for Combating Selection Bias During High-Dimensional Visualization [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Large and high-dimensional real-world datasets are being gathered across a wide range of application disciplines to enable data-driven decision making. Interactive data visualization can play a critical role in allowing domain experts to select and analyze data from these large collections. However, there is a critical mismatch between the very large number of dimensions in complex real-world datasets and the much smaller number of dimensions that can be concurrently visualized using modern techniques. This gap in dimensionality can result in high levels of selection bias that go unnoticed by users. The bias can in turn threaten the very validity of any subsequent insights. This article describes Adaptive Contextualization (AC), a novel approach to interactive visual data selection that is specifically designed to combat the invisible introduction of selection bias. The AC approach (1) monitors and models a user’s visual data selection activity, (2) computes metrics over that model to quantify the amount of selection bias after each step, (3) visualizes the metric results, and (4) provides interactive tools that help users assess and avoid bias-related problems. This article expands on an earlier article presented at ACM IUI 2016 [16] by providing a more detailed review of the AC methodology and additional evaluation results.
@article{gotz_adaptive_2017,
title = {Adaptive {Contextualization} {Methods} for {Combating} {Selection} {Bias} {During} {High}-{Dimensional} {Visualization}},
volume = {7},
issn = {2160-6455},
url = {http://doi.acm.org/10.1145/3009973},
doi = {10.1145/3009973},
abstract = {Large and high-dimensional real-world datasets are being gathered across a wide range of application disciplines to enable data-driven decision making. Interactive data visualization can play a critical role in allowing domain experts to select and analyze data from these large collections. However, there is a critical mismatch between the very large number of dimensions in complex real-world datasets and the much smaller number of dimensions that can be concurrently visualized using modern techniques. This gap in dimensionality can result in high levels of selection bias that go unnoticed by users. The bias can in turn threaten the very validity of any subsequent insights. This article describes Adaptive Contextualization (AC), a novel approach to interactive visual data selection that is specifically designed to combat the invisible introduction of selection bias. The AC approach (1) monitors and models a user’s visual data selection activity, (2) computes metrics over that model to quantify the amount of selection bias after each step, (3) visualizes the metric results, and (4) provides interactive tools that help users assess and avoid bias-related problems. This article expands on an earlier article presented at ACM IUI 2016 [16] by providing a more detailed review of the AC methodology and additional evaluation results.},
number = {4},
urldate = {2019-12-16},
journal = {ACM Trans. Interact. Intell. Syst.},
author = {Gotz, David and Sun, Shun and Cao, Nan and Kundu, Rita and Meyer, Anne-Marie},
month = nov,
year = {2017},
keywords = {WHEN - Real-Time Applications, HOW - Pattern Analysis, WHY - Real-time or post-hoc quantification and re-application, WHY - User Behaviour, User Characteristics, User Modelling, Type of Work: Empirical Study, Type of Work: Theory \& Model},
pages = {17:1--17:23},
file = {Gotz et al. - 2017 - Adaptive Contextualization Methods for Combating S.pdf:C\:\\Users\\conny\\Zotero\\storage\\NF7ZTDQE\\Gotz et al. - 2017 - Adaptive Contextualization Methods for Combating S.pdf:application/pdf}
}
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