用真实数据教统计中的公平性问题，让歧视分析变直观。
dsld: A Socially Relevant Tool for Teaching Statistics
- 开发R包dsld，整合歧视分析的图表与统计方法
- 通过真实案例展示混杂效应和模型偏差
- 配套80页教材，适合教学与法律从业者使用
数据科学在统计教育中的影响日益增强，亟需能通过现实应用展现核心概念的工具。我们推出R包「Data Science Looks At Discrimination」（dsld），提供一套完整的分析与可视化方法，用于研究涉及种族、性别、年龄等属性的歧视问题。将公平性分析作为教学工具，帮助教师通过实际案例展示混杂效应、模型偏差等关键概念。配套的80页Quarto电子书指导学生与法律专业人士理解原理并应用于真实数据。本文描述了包内函数的实现方式，并通过实例演示其使用。同时提供Python接口。
原文摘要 · Abstract (English)
The growing influence of data science in statistics education requires tools that make key concepts accessible through real-world applications. We introduce "Data Science Looks At Discrimination" (dsld), an R package that provides a comprehensive set of analytical and graphical methods for examining issues of discrimination involving attributes such as race, gender, and age. By positioning fairness analysis as a teaching tool, the package enables instructors to demonstrate confounder effects, model bias, and related topics through applied examples. An accompanying 80-page Quarto book guides students and legal professionals in understanding these principles and applying them to real data. We describe the implementation of the package functions and illustrate their use with examples. Python interfaces are also available.
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