Algorithms
aif360.algorithms.preprocessing
Disparate impact remover is a preprocessing technique that edits feature values increase group fairness while preserving rank-ordering within groups [1]_. |
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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]_. |
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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]_. |
Reweighing is a preprocessing technique that Weights the examples in each (group, label) combination differently to ensure fairness before classification [4]_. |
aif360.algorithms.inprocessing
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]_. |
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Wraps an instance of an |
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Model is an algorithm for learning classifiers that are fair with respect to rich subgroups. |
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The meta algorithm here takes the fairness metric as part of the input and returns a classifier optimized w.r.t. |
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Prejudice remover is an in-processing technique that adds a discrimination-aware regularization term to the learning objective [6]_. |
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Exponentiated gradient reduction for fair classification. |
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Grid search reduction for fair classification or regression. |
aif360.algorithms.postprocessing
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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]_. |
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]_. |
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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]_. |
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A collection of algorithms for construction of fair ranked candidate lists. |
aif360.algorithms
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Abstract base class for transformers. |