algorithms.preprocessing.DisparateImpactRemover([…]) Disparate impact remover is a preprocessing technique that edits feature values increase group fairness while preserving rank-ordering within groups [1]_.
algorithms.preprocessing.LFR(…[, k, Ax, …]) Learning fair representations is a pre-processing technique that finds a latent representation which encodes the data well but obfuscates information about protected attributes [2]_.
algorithms.preprocessing.OptimPreproc(…[, …]) Optimized preprocessing is a preprocessing technique that learns a probabilistic transformation that edits the features and labels in the data with group fairness, individual distortion, and data fidelity constraints and objectives [3]_.
algorithms.preprocessing.Reweighing(…) Reweighing is a preprocessing technique that Weights the examples in each (group, label) combination differently to ensure fairness before classification [4]_.


algorithms.inprocessing.AdversarialDebiasing(…) Adversarial debiasing is an in-processing technique that learns a classifier to maximize prediction accuracy and simultaneously reduce an adversary’s ability to determine the protected attribute from the predictions [5]_.
algorithms.inprocessing.ARTClassifier(…) Wraps an instance of an art.classifiers.Classifier to extend Transformer.
algorithms.inprocessing.GerryFairClassifier([…]) Model is an algorithm for learning classifiers that are fair with respect to rich subgroups.
algorithms.inprocessing.MetaFairClassifier([…]) The meta algorithm here takes the fairness metric as part of the input and returns a classifier optimized w.r.t.
algorithms.inprocessing.PrejudiceRemover([…]) Prejudice remover is an in-processing technique that adds a discrimination-aware regularization term to the learning objective [6]_.
algorithms.inprocessing.ExponentiatedGradientReduction(…) Exponentiated gradient reduction for fair classification.
algorithms.inprocessing.GridSearchReduction(…) Grid search reduction for fair classification or regression.


algorithms.postprocessing.CalibratedEqOddsPostprocessing(…) Calibrated equalized odds postprocessing is a post-processing technique that optimizes over calibrated classifier score outputs to find probabilities with which to change output labels with an equalized odds objective [7]_.
algorithms.postprocessing.EqOddsPostprocessing(…) Equalized odds postprocessing is a post-processing technique that solves a linear program to find probabilities with which to change output labels to optimize equalized odds [8]_ [9]_.
algorithms.postprocessing.RejectOptionClassification(…) Reject option classification is a postprocessing technique that gives favorable outcomes to unpriviliged groups and unfavorable outcomes to priviliged groups in a confidence band around the decision boundary with the highest uncertainty [10]_.


algorithms.Transformer(**kwargs) Abstract base class for transformers.