data requirements
access to training data
Requires the original training dataset
42 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Prototype and Criticism Models | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Prototype and criticism methods summarise a dataset with a small set of real examples. Prototypes are instances chosen... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +5 | Any | Influence functions estimate which training examples most affected a model's prediction on a particular test example.... | ||
| RuleFit | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | RuleFit builds a predictive model that a person can read: a short list of if-then rules plus a few ordinary feature... | ||
| 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... | ||
| GAN-Based Tabular Synthetic Data | Algorithmic | Architecture/neural Networks/generative/gan Architecture/neural Networks/generative/vae +3 | Tabular | GAN-based tabular synthetic data generation trains a generative model on a real table, then samples new rows from it to... | ||
| 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... | ||
| Sequential CART Synthesis | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular | Sequential CART synthesis creates an artificial copy of a confidential microdata set (records about individual people or... | ||
| Differential Privacy | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Differential privacy puts a mathematical limit on what a published result reveals about any one person. The data holder... | ||
| Deep Ensembles | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Deep ensembles estimate how uncertain a neural network's predictions are by training several copies of the same network... | ||
| Bootstrap Ensembles | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Bootstrap ensembles estimate how much a model's predictions depend on the particular sample of data it was trained on.... | ||
| Jackknife Resampling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Jackknife resampling, also called leave-one-out resampling, shows how much a result computed from a dataset depends on... | ||
| 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... | ||
| Model Cards | Documentation | Architecture/model Agnostic Requirements/black Box | Any | A model card is a short structured document published with a trained model, stating what the model is for and what it... | ||
| 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... | ||
| Disparate Impact Remover | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Disparate Impact Remover is a pre-processing method that edits the numerical features of a dataset so that they reveal... | ||
| 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... | ||
| Attribute Removal (Fairness Through Unawareness) | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Attribute removal, also called fairness through unawareness, trains and runs a model without the protected attributes,... | ||
| Fair Adversarial Networks | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Fair Adversarial Networks is a pre-processing method, proposed by Cevora (2020), that rewrites a dataset so that a... |
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