5. Tutorials

This section provides interactive tutorials demonstrating how to use FairLangProc for bias detection and mitigation in NLP models.

Tip

All tutorials are available as Jupyter notebooks. You can run them locally by cloning the repository or access them on Google Colab.

git clone https://github.com/arturo-perez-peralta/FairLangProc

Then navigate to the notebooks/ directory and open the notebooks in Jupyter.

5.1. Datasets

Demo Datasets - Open on GitHub

Learn how to load and explore bias benchmark datasets.

  • Load BBQ, StereoSet, BOLD, and other datasets

  • Explore dataset structure and attributes

5.2. Metrics

Demo Metrics - Open on GitHub

Measure bias using various fairness metrics.

  • WEAT for embedding-level bias

  • LPBS, CBS, CPS, AUL for probability-level bias

  • DR, ST, HONEST for generated text bias

5.3. Bias Mitigation

Demo Debiasing - Open on GitHub

Apply debiasing algorithms to reduce model bias.

  • Pre-processing with CDA, BLIND and projection-based debiasing.

  • In-procesing with ADELE and regularizers.

  • Intra-processing with MoDDiffy and EAT scaling.

5.4. End-to-End Workflow

Demo Processors - Open on GitHub

Use fairness processor to debiase LLMs in the GLUE tasks.

  • Use WEAT to measure bias.

  • Use the different processors to mitigate embedding-level bias.

  • Measure the impact on the performance metrics of the GLUE datasets.

5.5. Notebook Index

Notebook

Description

DemoDatasets.ipynb

Load and explore bias benchmark datasets

DemoMetrics.ipynb

Measure bias with WEAT, LPBS, CBS, HONEST, etc.

DemoDebiasing.ipynb

Apply debiasing algorithms (CDA, Projection, ADELE)

DemoProcessors.ipynb

Use in-processing and intra-processing bias mitigators

5.6. Requirements

To run the notebooks locally, you need to install FairLangProc and download the datasets:

pip install FairLangProc

Note

Some notebooks require significant computational resources (GPU recommended). For GPU acceleration, ensure CUDA is installed and configured.

# Verify CUDA availability
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"