NDLI: Adaptive Contextualization: Combating Bias During High-Dimensional Visualization and Data Selection

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Adaptive Contextualization: Combating Bias During High-Dimensional Visualization and Data Selection

Content Provider	ACM Digital Library
Author	Gotz, David Sun, Shun Cao, Nan
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. In this paper, we present Adaptive Contextualization (AC), a novel approach to interactive visual data selection that is specifically designed to combat the invisible introduction of selection bias. Our 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. We also share results from a user study which demonstrate the effectiveness of our technique.
Starting Page	85
Ending Page	95
Page Count	11
File Format	PDF
ISBN	9781450341370
DOI	10.1145/2856767.2856779
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-03-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Visualization Exploratory analysis Visual analytics Intelligent visual interfaces
Content Type	Text
Resource Type	Article

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