data requirements
labelled data
Requires datasets with ground truth labels
46 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Permutation Importance | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular | Permutation Importance measures how much a trained model relies on each input feature. For each feature, the values in a... | ||
| Permutation Tests | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | A permutation test asks how likely an observed result would be if there were no real relationship in the data. The... | ||
| Adversarial Debiasing | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | Adversarial debiasing trains a model so that its predictions carry as little information as possible about a protected... | ||
| Statistical Oversampling Methods | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Statistical oversampling methods rebalance a training set by adding synthetic examples of an under-represented category... | ||
| Quantile Regression | Algorithmic | Architecture/linear Models/regression Architecture/neural Networks +4 | Tabular | Quantile regression predicts chosen percentiles of an outcome, such as the 10th, 50th and 90th, rather than only its... | ||
| Cross-validation | Testing | Architecture/model Agnostic Paradigm/supervised +1 | Any | Cross-validation estimates how well a model will perform on data it has not seen, and how much that estimate depends on... | ||
| Area Under Precision-Recall Curve | Metric | Architecture/model Agnostic Paradigm/supervised +2 | Any | Area Under Precision-Recall Curve (AUPRC) summarises how well a classifier finds the cases that matter when those cases... | ||
| Monotonicity Constraints | Algorithmic | Architecture/neural Networks Architecture/probabilistic/gaussian Processes +3 | Tabular | Monotonicity constraints require a model's prediction to move only one way as a chosen input rises, with the other... | ||
| Generalized Additive Models | Algorithmic | Architecture/linear Models/gam Paradigm/parametric +2 | Tabular | A generalised additive model (GAM) builds its prediction by adding a baseline and one smooth curve for each input... | ||
| Concept Activation Vectors | Algorithmic | Architecture/neural Networks Requirements/gradient Access +2 | Any | A concept activation vector (CAV) is a direction inside a neural network that stands for a human idea such as 'stripes'.... | ||
| Reweighing | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Reweighing is a fairness method, presented by Kamiran and Calders (2012), that adjusts the training data before any... | ||
| Fairness GAN | Algorithmic | Architecture/neural Networks/generative/gan Paradigm/generative +3 | Any | Fairness GAN is a pre-processing method that generates a synthetic training dataset in which decisions no longer track a... | ||
| Relabelling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Relabelling, also called data massaging, is a pre-processing method proposed by Kamiran and Calders. It changes some of... | ||
| Preferential Sampling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Preferential Sampling is a pre-processing method, proposed by Kamiran and Calders, that rebalances a training set so... | ||
| Prejudice Remover Regulariser | Algorithmic | Architecture/linear Models/logistic Architecture/probabilistic +4 | Tabular | The Prejudice Remover Regulariser, proposed by Kamishima and colleagues (2012), trains a classifier with an extra... | ||
| Meta Fair Classifier | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | The meta fair classifier is a general-purpose training algorithm for fair classification. The user chooses a fairness... | ||
| Exponentiated Gradient Reduction | Algorithmic | Architecture/model Agnostic Paradigm/discriminative +4 | Any | Exponentiated gradient reduction trains a classifier that satisfies a chosen fairness criterion, such as demographic... | ||
| Learning Adversarially Fair and Transferable Representations | Algorithmic | Architecture/neural Networks Paradigm/parametric +4 | Image Tabular | Learning Adversarially Fair and Transferable Representations (LAFTR) trains an encoder that turns each record into a... | ||
| Multi-Accuracy Boosting | Algorithmic | Architecture/ensemble Paradigm/discriminative +2 | Any | Multi-accuracy boosting is a post-processing method that corrects a trained classifier's systematic errors on subgroups... | ||
| Threshold Optimiser | Algorithmic | Architecture/model Agnostic Paradigm/discriminative +2 | Any | Threshold Optimiser is a post-processing method, introduced by Hardt, Price and Srebro (2016), that adjusts a trained... |
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