Fairness
Post Processing
Techniques that adjust decisions or thresholds after training so that outcomes or error rates match across groups.
4 techniques
4 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| 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... | ||
| Reject Option Classification | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Reject Option Classification is a post-processing method, proposed by Kamiran and colleagues (2012), that changes some... | ||
| Calibration with Equality of Opportunity | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Calibration with equality of opportunity is a post-processing step that adjusts a classifier's scores so that two groups... |
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