# dsld: A Socially Relevant Tool for Teaching Statistics

arXiv: https://arxiv.org/abs/2411.04228 · HTML: https://mlsanity.com/paper/2411.04228/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Improving Fair Training under Correlation Shifts](https://mlsanity.com/paper/2302.02323/) (2023)
- [A Maximal Correlation Approach to Imposing Fairness in Machine Learning](https://mlsanity.com/paper/2012.15259/) (2020)
- [fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation](https://mlsanity.com/paper/2104.00507/) (2021)
- [fairml: A Statistician's Take on Fair Machine Learning Modelling](https://mlsanity.com/paper/2305.02009/) (2023)
- [A Novel Regularization Approach to Fair ML](https://mlsanity.com/paper/2208.06557/) (2022)
- [Practical considerations for sandwich variance estimation in two-stage regression settings](https://mlsanity.com/paper/2209.10061/) (2022)

## Abstract (arXiv)

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.