Fairness

Group

Focuses on fairness between demographic groups

14 techniques in this subcategory

14 techniques
GoalsModelsData TypesDescription
Adversarial Debiasing
Algorithmic
Neural Network
Any
Adversarial debiasing reduces bias by training models using a competitive adversarial setup, similar to Generative...
Fairness GAN
Algorithmic
GAN
Any
A data generation technique that employs Generative Adversarial Networks (GANs) to create fair synthetic datasets by...
Relabelling
Procedural
Model Agnostic
Any
A preprocessing fairness technique that modifies class labels in training data to achieve equal positive outcome rates...
Preferential Sampling
Procedural
Model Agnostic
Any
A preprocessing fairness technique developed by Kamiran and Calders that addresses dataset imbalances by re-sampling...
Fair Adversarial Networks
Algorithmic
Neural Network
CNN
+1
Any
An in-processing fairness technique that employs adversarial training with dual neural networks to learn fair...
Prejudice Remover Regulariser
Algorithmic
Logistic Regression
Probabilistic
Tabular
An in-processing fairness technique that adds a fairness penalty to machine learning models to reduce bias against...
Meta Fair Classifier
Algorithmic
Model Agnostic
Any
An in-processing fairness technique that employs meta-learning to modify any existing classifier for optimising fairness...
Exponentiated Gradient Reduction
Algorithmic
Model Agnostic
Any
An in-processing fairness technique based on Agarwal et al.'s reductions approach that transforms fair classification...
Fair Transfer Learning
Algorithmic
Model Agnostic
Any
An in-processing fairness technique that adapts pre-trained models from one domain to another whilst explicitly...
Multi-Accuracy Boosting
Algorithmic
Model Agnostic
Ensemble
Any
An in-processing fairness technique that employs boosting algorithms to improve accuracy uniformly across demographic...
Equalised Odds Post-Processing
Algorithmic
Model Agnostic
Any
A post-processing fairness technique based on Hardt et al.'s seminal work that adjusts classification thresholds after...
Reject Option Classification
Algorithmic
Model Agnostic
Any
A post-processing fairness technique that modifies predictions in regions of high uncertainty to favour disadvantaged...
Calibration with Equality of Opportunity
Algorithmic
Model Agnostic
Any
A post-processing fairness technique that adjusts model predictions to achieve equal true positive rates across...
Equal Opportunity Difference
Metric
Model Agnostic
Any
A fairness metric that quantifies discrimination by measuring the difference in true positive rates (recall) between...
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