A Framework for Cognitive Bias Detection and Feedback in a Visual Analytics Environment
A Framework for Cognitive Bias Detection and Feedback in a Visual Analytics Environment
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视觉分析环境中的认知偏差检测和反馈框架
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. Albert
中科院分区：
文献类型：
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作者：
A. Nussbaumer;K. Verbert;Eva;M. Bedek;D. Albert
本文提出了一个框架，支持视觉分析环境中的犯罪分析的认知偏见的检测和缓解。犯罪分析师经常使用可视化分析环境来分析大型数据集，以获得对犯罪事件和犯罪事件模式的见解，并得出结论和做出决策。然而，由于人类认知的本质，这些认知过程可能会导致系统性错误，即所谓的认知偏差。在情报领域，最突出和最相关的认知偏差是确认偏差，在这种情况下，分析师会谨慎地考虑和选择支持最初预期和假设的信息。本文提出的框架描述了一个模型，如何可能发生的确认偏差可以自动检测，而分析师利用视觉环境。此外，基于这些信息，采用不同的反馈方法来支持和鼓励缓解确认偏差。这一框架正在进行中，其中包括研究目标和方向、框架设计、初步实施、进一步发展和整合计划以及以用户为中心的评价。
This paper presents a framework that supports the detection and mitigation of cognitive biases in visual analytics environments for criminal analysis. Criminal analysts often use visual analytics environments for their analysis of large data sets, for gaining insights on criminal events and patterns of criminal events, and for drawing conclusions and making decisions. However, due to the nature of human cognition, these cognitive processes may lead to systematic errors, so-called cognitive biases. The most prominent and relevant cognitive bias in the intelligence field is the confirmation bias, in which an analyst disproportionally considers and selects information that supports the initial expectation and hypothesis. The framework presented in this paper describes a model, how the possible occurence of the confirmation bias can be detected automatically, while the analyst makes use of the visual environment. Moreover, based on this information, different feedback methods are employed that support and encourage the mitigation of the confirmation bias. This framework is in a work-in-progress state and contains research objectives and directions, the framework design, initial implementations, plans for further development and integration, as well as user-centric evaluation.