dsld: A Socially Relevant Tool for Teaching Statistics

The growing power of data science can play a crucial role in addressing social discrimination, necessitating nuanced understanding and effective mitigation strategies for biases. "Data Science Looks At Discrimination" (DSLD) is an R and Python package designed to provide users with a comprehensive toolkit of statistical and graphical methods for assessing possible discrimination related to protected groups such as race, gender, and age. The package addresses critical issues by identifying and mitigating confounders and reducing bias against protected groups in prediction algorithms.In educational settings, DSLD offers instructors powerful tools to teach statistical principles through motivating real world examples of discrimination analysis. The inclusion of an 80 page Quarto book further supports users from statistics educators to legal professionals in effectively applying these analytical tools to real world scenarios.

@article{abdullah2025_2411.04228,
  title={ dsld: A Socially Relevant Tool for Teaching Statistics },
  author={ Taha Abdullah and Arjun Ashok and Brandon Zarate and Shubhada Martha and Billy Ouattara and Norman Matloff and Aditya Mittal },
  journal={arXiv preprint arXiv:2411.04228},
  year={ 2025 }
}