This repository contains source code and slides for the talk "Fairness in machine learning" at the Salt Lake City R User Group in December 2023.

• The slides for the talk are available here.
• The example analysis that these slides are based on is available here.
• Another example analysis demonstrating a fairness-oriented modeling workflow is here.

To learn more about data science and machine learning with R,

• Data science with the tidyverse: r4ds.hadley.nz
• Machine learning with tidymodels: tmwr.org
• More example notebooks with tidymodels: tidymodels.org

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These slides use the development version of some tidymodels packages. To install the packages needed to run this code yourself, use the following R code:

# if needed:
install.packages("pak")

# install the tidymodels meta-package
pak::pak("tidymodels")

# install the development versions of yardstick and tune
pak::pak(paste0("tidymodels/", c("yardstick", "tune"))

# install the source data
pak::pak("detectors")

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In this repository,

• index.qmd contains the source code for the slides. The slides use images in the /figures directory.
• /docs is auto-generated from index.qmd. Content in that folder is likely unhelpful for a human reader, and is better viewed at the links above. :)