dsld: A Socially Relevant Tool for Teaching Statistics

TL;DR

The paper introduces 'dsld', an R package and educational resource designed to teach fairness and discrimination analysis in data science, making complex concepts accessible through real-world examples and graphical tools.

Contribution

It presents a novel educational package that combines analytical tools and a comprehensive guide to facilitate teaching fairness analysis in statistics education.

Findings

The package effectively demonstrates confounder effects and model bias.

It includes an 80-page guide for practical understanding.

Python interfaces expand accessibility.

Abstract

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.