DDS8501

R for data science (2nd ed.).

Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science (2nd ed.). O’Reilly Media. https://r4ds.hadley.nz/ 

• Read Chapter 10: Exploratory Data Analysis 

Variations and typical values are explored. 

Fairness and Machine Learning: Limitations and Opportunities

Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT Press. Retrieved from https://fairmlbook.org/  

Core concepts of statistical parity, equal opportunity, predictive parity; ethical framing for subgroup analysis). 

A Survey on Bias and Fairness in Machine Learning

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457188 

Contains broad taxonomy of bias sources/metrics; great background for fairness diagnostics.