dsld: A Socially Relevant Tool for Teaching Statistics
This tool addresses the problem of teaching fairness and bias concepts in statistics education for instructors and learners, though it is incremental as it packages existing methods into an educational framework.
The authors tackled the need for accessible data science tools in statistics education by developing the dsld R package, which provides analytical and graphical methods for examining discrimination issues through real-world applications, accompanied by an 80-page guide for students and professionals.
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.