Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics

Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics
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警告,可能会出现偏差:一种检测交互式视觉分析中认知偏差的提议方法

DOI:
10.1109/vast.2017.8585669
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发表时间:
2017
期刊:
IEEE Visual Analytic Science and Technology (VAST
影响因子:
--
通讯作者:
Endert, Alex
Endert, Alex
中科院分区:
--
文献类型:
--
作者:
Wall, Emily;Blaha, Leslie M.;Franklin, Lyndsey;Endert, Alex

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视觉分析工具结合了人机循环系统中人类和机器的互补优势。人类通过分析模型为这种话语提供了宝贵的领域专业知识和意义建构能力;然而,很少有人考虑到人类固有的偏见可能会影响视觉分析过程。在本文中,我们建立了一个通过人机交互系统考虑偏差评估的概念框架,并为偏差测量奠定了理论基础。我们提出了六个初步指标来系统地检测和量化用户交互中的偏差,并演示如何在现有的视觉分析系统 InterAxis 中实施这些指标。我们讨论了视觉分析系统如何使用我们提出的指标来通过让用户在整个分析过程中意识到偏见过程来减轻认知偏见的负面影响。
Visual analytic tools combine the complementary strengths of humans and machines in human-in-the-loop systems. Humans provide invaluable domain expertise and sensemaking capabilities to this discourse with analytic models; however, little consideration has yet been given to the ways inherent human biases might shape the visual analytic process. In this paper, we establish a conceptual framework for considering bias assessment through human-in-the-loop systems and lay the theoretical foundations for bias measurement. We propose six preliminary metrics to systematically detect and quantify bias from user interactions and demonstrate how the metrics might be implemented in an existing visual analytic system, InterAxis. We discuss how our proposed metrics could be used by visual analytic systems to mitigate the negative effects of cognitive biases by making users aware of biased processes throughout their analyses.
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