Fairness
In Processing
Techniques that build a fairness constraint, penalty or adversary into model training.
6 techniques
6 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| 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... | ||
| 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... | ||
| Fair Bayesian Optimisation | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Fair Bayesian Optimisation (FairBO) tunes a model's hyperparameters, the settings fixed before training such as tree... |
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