expertise needed
low
Can be applied with basic technical knowledge and standard tools
8 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Coefficient Magnitudes (in Linear Models) | Metric | Linear Model | Tabular | Coefficient Magnitudes assess feature influence in linear models by examining the absolute values of their coefficients.... | ||
| Individual Conditional Expectation Plots | Visualization | Model Agnostic | Any | ICE plots display the predicted output for individual instances as a function of a feature, with all other features held... | ||
| Intrinsically Interpretable Models | Algorithmic | Tree Based Linear | Any | Intrinsically interpretable models are machine learning algorithms that are transparent by design, allowing users to... | ||
| Reweighing | Algorithmic | Model Agnostic | Any | Reweighing is a pre-processing technique that mitigates bias by assigning different weights to training examples based... | ||
| Preferential Sampling | Procedural | Model Agnostic | Any | A preprocessing fairness technique developed by Kamiran and Calders that addresses dataset imbalances by re-sampling... | ||
| Attribute Removal (Fairness Through Unawareness) | Algorithmic | Model Agnostic | Any | Attribute Removal (Fairness Through Unawareness) ensures fairness by completely excluding protected attributes such as... | ||
| Equal Opportunity Difference | Metric | Model Agnostic | Any | A fairness metric that quantifies discrimination by measuring the difference in true positive rates (recall) between... | ||
| Average Odds Difference | Metric | Model Agnostic | Any | Average Odds Difference measures fairness by calculating the average difference in both false positive rates and true... |
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