data requirements
labelled data
Requires datasets with ground truth labels
16 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Integrated Gradients | Algorithmic | Neural Network | Any | Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of... | ||
| Area Under Precision-Recall Curve | Algorithmic | Model Agnostic | Any | Area Under Precision-Recall Curve (AUPRC) measures model performance by plotting precision (the proportion of positive... | ||
| Monotonicity Constraints | Algorithmic | Tree Based Gaussian Process | Tabular | Monotonicity constraints enforce consistent directional relationships between input features and model predictions,... | ||
| Generalized Additive Models | Algorithmic | GAM Linear Model | Tabular | An intrinsically interpretable modelling technique that extends linear models by allowing flexible, nonlinear... | ||
| 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... | ||
| 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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