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

合作研究：HCC：媒介：可视化数据分析中的建模和减轻确认偏差

• 批准号: 2311574
• 负责人: Emily Wall
• 金额: $ 68.31万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2027-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311574&HistoricalAwards=false
• 关键词: Collaborative; Research; HCC; Medium; Modeling

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.

做出决策时，人们很容易被数据淹没，例如确定哪种医疗保健待遇是合适的或要投票的政治候选人。当数据不知所措时，人们倾向于以支持其先前信念的方式寻求和解释信息。这种现象通常称为确认偏差。在数据通信和视觉分析中，即使对于经验丰富的分析师来说，确认偏差也可能是邪恶的。尽管存在一个误解，即统计模型和可视化呈现客观真理，但实际上，收集，处理，分析和显示数据中的选择可以使人们过分依赖他们先前存在的信念。该项目将通过（1）创建模型在数据分析中密切检查确认偏见，以显示现有信念和分析目标如何影响数据驱动的决策，以及（2）设计新颖的分析界面，以帮助分析师通过明智地提出可能证明一种信念的证据来帮助分析人员做出较少的偏见决策。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，研究人员和公众对视觉数据通信和解释中的确认偏见进行思考。该项目旨在提高对确认偏差如何在现实世界视觉数据分析任务中表现出来的理解，并制定和评估偏见减轻干预措施。该项目围绕四个研究推力进行结构。由于测量确认偏见需要捕获个人的信念，研究人员将首先研究实验方法，以准确捕获一个人的信念和心理表征，以了解分析环境（推力I）。然后，研究人员将利用这些方法学发现来衡量和模拟低级视觉分析任务中确认偏差的效果，例如查找相关性（推力II），然后检查在更现实的分析设置（推力III）中这些任务的高级组成。最后，研究人员将设计和开发四种偏见减少干预措施，以纳入现实世界的视觉分析工具，例如数据旅行者和jupyter笔记本电脑，招募专业分析师在数字现场研究（Throust IV）中评估它们。这项研究议程将提高对确认偏见的理解，并提供承诺的干预措施，以授权数据分析师做出更好的决策。研究人员还将开发课程和举措，将计算机科学，心理学和道德规范融合在一起，以围绕视觉数据分析提高实践和教育。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和更广泛的影响来审查标准，被视为通过评估来获得珍贵的支持。