Collaborative Research: HCC: Medium: Modeling and Mitigating Confirmation Bias in Visual Data Analysis

人们在做决策时很容易被数据淹没，比如决定哪种医疗保健治疗是合适的，或者投票给哪位政治候选人。当被数据淹没时，人们倾向于以支持他们先前存在的信念的方式寻求和解释信息。这种现象通常被称为确认偏差。在数据通信和可视化分析中，确认偏差可能特别邪恶，即使是经验丰富的分析师也是如此。虽然有一种误解，认为统计模型和可视化呈现客观事实，但实际上，在收集，处理，分析和呈现数据时的选择可能会使人们过度依赖他们先前存在的信念。该项目将通过以下方式仔细研究数据分析中的确认偏差：（1）创建模型，展示现有信念和分析目标如何影响数据驱动的决策;（2）设计新颖的分析界面，通过智能地提出可能反驳信念的证据，帮助分析师做出更少的偏见决策。该项目团队还将创建教育材料并收集经验数据集，以帮助数据分析师，研究人员和公众思考视觉数据通信和解释中的确认偏差。该项目旨在增加对确认偏差如何在现实世界的视觉数据分析任务中表现的理解，并开发和评估偏差缓解干预措施。该项目围绕四个研究方向展开。由于测量确认偏差需要捕捉个人的信念，研究人员将首先研究实验方法，以准确捕捉一个人在分析环境中对数据模式和趋势的信念和心理表征。然后，研究人员将利用这些方法学的发现来测量和建模确认偏差在低级别视觉分析任务中的影响，例如寻找相关性（推力II），然后在更现实的分析环境中检查这些任务的高级别组成（推力III）。最后，研究人员将设计和开发四种偏差缓解干预措施，并将其纳入现实世界的视觉分析工具，如Data Voyager和Moneyter Notebooks，招募专业分析师在数字实地研究中对其进行评估。该研究议程将促进对确认偏差的理解，并提供有希望的干预措施，使数据分析师能够做出更好的决策。研究人员还将开发课程和计划，将计算机科学，心理学和伦理学结合起来，以推进可视化数据分析的实践和教育。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

People can easily be overwhelmed with data when making decisions, such as deciding which healthcare treatment is appropriate or which political candidate to vote for. When overwhelmed by data, people tend to seek and interpret information in a way that supports their preexisting beliefs. This phenomenon is often referred to as confirmation bias. In data communication and visual analytics, confirmation bias can be especially nefarious, even for experienced analysts. Although there is a misconception that statistical models and visualizations present objective truths, in reality, choices in the collection, handling, analysis, and presentation of data can bias people into overly relying on their pre-existing beliefs. This project will closely examine confirmation bias in data analysis by (1) creating models that show how existing beliefs and analytic goals can impact data-driven decision-making, and (2) designing novel analytic interfaces that help analysts make less biased decisions by intelligently suggesting evidence that may disprove a belief. The project team will also create educational materials and collect empirical datasets to help data analysts, researchers, and members of the public think about confirmation bias in visual data communication and interpretation. This project aims to increase understanding of how confirmation bias manifests in real-world visual data analysis tasks and to develop and evaluate bias-mitigation interventions. The project is structured around four research thrusts. As measuring confirmation bias requires capturing an individual’s beliefs, the researchers will first investigate experimental methods to accurately capture a person's beliefs and mental representations about data patterns and trends in an analytic setting (Thrust I). The researchers will then leverage these methodological findings to measure and model the effect of confirmation bias in low-level visual analytic tasks such as finding correlations (Thrust II), then examine higher-level compositions of these tasks in more realistic analysis settings (Thrust III). Finally, the researchers will design and develop four bias-mitigation interventions to be incorporated into real-world visual analytic tools such as Data Voyager and Jupyter Notebooks, recruiting professional analysts to evaluate them in digital field studies (Thrust IV). This research agenda will advance the understanding of confirmation bias and provide promising interventions to empower data analysts to make better decisions. The researchers will also develop coursework and initiatives that bring together computer science, psychology, and ethics to advance practice and education around visual data analytics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.