Fairness Analysis and Python Libraries

Fairness Analysis and Python Libraries
- Python for Integrated Circuits -
- An Online Book -

Python for Integrated Circuits http://www.globalsino.com/ICs/

Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

=================================================================================

Fairness in a company or organization is often subjective based on whose eyes we view the matter in question from. Analysis with Python can uncover the Truth and ensure fairness to make unbiased decisions that do not discriminate against certain groups of people and ensures the same opportunities for different groups of people.

The most popular Python libraries for AI fairness are:
          i) FairSight: Assist in achieving fair decision making.
          ii) AIF360: for detecting and mitigating bias in ML.
          iii) Fairlearn: for fair ML that supports various fairness metrics.
          iv) Themis-ML: for enforcing group fairness in ML.
          v) Debiaswe: for debiasing word embeddings in natural language processing.
          vi) ML-fairness-gym: for training and evaluating fair reinforcement learning models.
          vii) fairness-in-ml: for fair and transparent ML.
          viii) AI Fairness 360 (AIF360): for detecting and mitigating bias in ML.
          ix) bias-correction: for removing bias from ML models.
          x) Fairness-comparison: benchmarking of fairness aware ML algorithms.
          xi) BlackBoxAuditing: contains a sample implementation of Gradient Feature Auditing (GFA).
          xii) Aequitas: For auditing ML models for discrimination and bias.
          xiii) fairness-indicators: Tensorflow's Fairness Evaluation and Visualization Toolkit.
          xiv) Responsible-AI-Toolbox: It has various group-fairness metrics across sensitive features.
          xv) LinkedIn Fairness Toolkit (LiFT): Scala/Spark library of fairness.
          xvi) Responsibly: Toolkit for Auditing and Mitigating Bias and Fairness.
          xvii) smclarify: Bias detection and mitigation for datasets and models.
          xviii) inFairness: PyTorch package to train and audit ML models for Individual Fairness.
          xix) Awesome-Fairness-in-AI: A curated list of Fairness in AI resources.
          xx) KDD-2021-Network-Fairness-Tutorial: KDD tutorial on Network Fairness

============================================

=================================================================================