Fairness
64 techniques
Ensuring AI systems treat different groups and individuals equitably.
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All techniques
64 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| SHapley Additive exPlanations | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an... | ||
| Sobol Indices | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Sobol Indices quantify how much each input feature contributes to the total variance in a model's predictions through... | ||
| Gradient Saliency | Algorithmic | Architecture/neural Networks Requirements/differentiable +1 | Image | Gradient saliency (also known as vanilla gradient saliency) produces visual explanations for image classification models... | ||
| Gradient-weighted Class Activation Mapping | Algorithmic | Architecture/neural Networks/convolutional Requirements/architecture Specific +2 | 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 | Architecture/model Agnostic Paradigm/supervised +3 | Any | Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +6 | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| Permutation Tests | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Permutation tests assess the statistical significance of observed results (such as model accuracy, feature importance,... | ||
| Demographic Parity Assessment | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Demographic Parity Assessment evaluates whether a model produces equal positive prediction rates across different... | ||
| Adversarial Debiasing | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | Adversarial debiasing reduces bias by training models using a competitive adversarial setup, similar to Generative... | ||
| Counterfactual Fairness Assessment | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's... | ||
| Sensitivity Analysis for Fairness | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Sensitivity Analysis for Fairness systematically evaluates how model predictions change when sensitive attributes or... | ||
| GAN-Based Tabular Synthetic Data | Algorithmic | Architecture/neural Networks/generative/gan Architecture/neural Networks/generative/vae +4 | Tabular | Generates synthetic tabular datasets using Generative Adversarial Networks, most commonly through architectures such as... | ||
| Statistical Oversampling Methods | Algorithmic | Requirements/model Agnostic Paradigm/supervised +1 | Tabular | A family of data augmentation techniques that generate synthetic minority-class examples through geometric interpolation... | ||
| Federated Learning | Algorithmic | Architecture/linear Models Architecture/neural Networks +4 | Any | Federated learning enables collaborative model training across multiple distributed parties (devices, organisations, or... | ||
| Differential Privacy | Algorithmic | Architecture/model Agnostic Requirements/black Box +1 | Any | Differential privacy provides mathematically rigorous privacy protection by adding carefully calibrated random noise to... | ||
| Quantile Regression | Algorithmic | Architecture/linear Models/regression Architecture/neural Networks +4 | Any | Quantile regression estimates specific percentiles (quantiles) of the target variable rather than just predicting the... | ||
| Conformal Prediction | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Conformal prediction provides mathematically guaranteed uncertainty quantification by creating prediction sets that... | ||
| Empirical Calibration | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Empirical calibration adjusts a model's predicted probabilities to match observed frequencies. For example, if events... | ||
| Temperature Scaling | Algorithmic | Architecture/neural Networks Paradigm/discriminative +3 | Any | Temperature scaling adjusts a model's confidence by applying a single parameter (temperature) to its predictions. When a... | ||
| Bootstrapping | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Bootstrapping estimates uncertainty by repeatedly resampling the original dataset with replacement to create many new... |
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