fairlib
Tutorials & Explanations
fairlib
Quick-start
Built-in Debiasing Methods
Interactive Demos
Using
fairlib
with Text inputs
Using
fairlib
with Image Inputs
Using
fairlib
with Structured Inputs
Visualization
Interactive Visualization
Manipulating Data Distribution
Adding Customized Datasets
Adding Customized Evaluation Metrics
Adding Customized NN Architecture
Adding Customized Debiasing Methods
Component Reference
Bias Detection
Bias Mitigation
Hyperparameter Tuning
Scripts Reference
fairlib
Cheat Sheet
Analysis Robustness to Label Distribution
API Reference
Analysis Module
Evaluator Module
DataLoader Module
Network Module
Debiasing Module
fairlib
»
Interactive Demos
Interactive Demos
Using
fairlib
with Text inputs
1. Installation
2. Prepare Dataset
3. Standard Usage
4. Analysis
5. Read More
Using
fairlib
with Image Inputs
Installation
Prepare Data
Train a Vanilla Model
Bias Mitigation
Using
fairlib
with Structured Inputs
Installation
Download and preprocess the COMPAS dataset
Train a Vanilla Model
Bias Mitigation
Visualization
Load experimental results
Basic Plot
Zoomed Plots
AUC - Performance-Fairness Tradeoff
Interactive Visualization
Load experimental results
Crete Plot
Manipulating Data Distribution
Load data
Analysis the loaded dataset distribution
Resample isntacnes based on their target labels and protected labels
Limitation and Extension