Fair machine learning models: estimation, tuning and prediction.
fairml implements key algorithms for learning machine learning models while enforcing fairness with respect to a set of observed sensitive (or protected) attributes.
Currently fairml implements the following algorithms (references below):
nclm(): the non-convex formulation of fair linear regression
model from Komiyama et al. (2018).
frrm(): my fair (linear) ridge regression model.
fgrrm(): my fair generalized (linear) ridge regression model.
zlrm(): the fair logistic regression with covariance
constraints from Zafar et al. (2019).
zlrm(): a fair linear regression with covariance
constraints following Zafar et al. (2019).
Komiyama J, Takeda A, Honda J, Shimao H (2018). "Nonconvex Optimization for
Regression with Fairness Constraints". Proceedints of the 35th International
Conference on Machine Learning (ICML), PMLR 80:2737--2746.
http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf
Zafar BJ, Valera I, Gomez-Rodriguez M, Gummadi KP (2019). "Fairness
Constraints: a Flexible Approach for Fair Classification". Journal of
Machine Learning Research, 30:1--42.
https://www.jmlr.org/papers/volume20/18-262/18-262.pdf