Fairness
53 techniques
Ensuring AI systems treat different groups and individuals equitably.
53 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| SHapley Additive exPlanations | Algorithmic | Model Agnostic | Any | SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an... | ||
| Sobol Indices | Algorithmic | Model Agnostic | Any | Sobol Indices quantify how much each input feature contributes to the total variance in a model's predictions through... | ||
| Saliency Maps | Algorithmic | Neural Network | Image | Saliency maps are visual explanations for image classification models that highlight which pixels in an image most... | ||
| Gradient-weighted Class Activation Mapping | Algorithmic | CNN | Image | Grad-CAM creates visual heatmaps showing which regions of an image a convolutional neural network focuses on when making... | ||
| Prototype and Criticism Models | Algorithmic | Model Agnostic | Any | Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes... | ||
| Influence Functions | Algorithmic | Model Agnostic | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| Demographic Parity Assessment | Algorithmic | Model Agnostic | Any | Demographic Parity Assessment evaluates whether a model produces equal positive prediction rates across different... | ||
| Adversarial Debiasing | Algorithmic | Neural Network | Any | Adversarial debiasing reduces bias by training models using a competitive adversarial setup, similar to Generative... | ||
| Counterfactual Fairness Assessment | Algorithmic | Model Agnostic | Any | Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's... | ||
| Sensitivity Analysis for Fairness | Algorithmic | Model Agnostic | Any | Sensitivity Analysis for Fairness systematically evaluates how model predictions change when sensitive attributes or... | ||
| Synthetic Data Generation | Algorithmic | Model Agnostic | Any | Synthetic data generation creates artificial datasets that aim to preserve the statistical properties, distributions,... | ||
| Federated Learning | Algorithmic | Model Agnostic | Any | Federated learning enables collaborative model training across multiple distributed parties (devices, organisations, or... | ||
| Differential Privacy | Algorithmic | Model Agnostic | Any | Differential privacy provides mathematically rigorous privacy protection by adding carefully calibrated random noise to... | ||
| Prediction Intervals | Algorithmic | Model Agnostic | Any | Prediction intervals provide a range of plausible values around a model's prediction, expressing uncertainty as 'the... | ||
| Quantile Regression | Algorithmic | Model Agnostic | Any | Quantile regression estimates specific percentiles (quantiles) of the target variable rather than just predicting the... | ||
| Conformal Prediction | Algorithmic | Model Agnostic | Any | Conformal prediction provides mathematically guaranteed uncertainty quantification by creating prediction sets that... | ||
| Empirical Calibration | Algorithmic | Model Agnostic | Any | Empirical calibration adjusts a model's predicted probabilities to match observed frequencies. For example, if events... | ||
| Temperature Scaling | Algorithmic | Neural Network | Any | Temperature scaling adjusts a model's confidence by applying a single parameter (temperature) to its predictions. When a... | ||
| Bootstrapping | Algorithmic | Model Agnostic | Any | Bootstrapping estimates uncertainty by repeatedly resampling the original dataset with replacement to create many new... | ||
| Jackknife Resampling | Algorithmic | Model Agnostic | Any | Jackknife resampling (also called leave-one-out resampling) assesses model stability and uncertainty by systematically... |
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