Fairness in Machine Learning: A Tutorial 🫂

Machine learning models can unintentionally perpetuate biases present in training data, leading to unfair outcomes. This tutorial explores fairness-aware techniques to address these issues and ensure equitable AI systems.

Key Concepts

1. Bias Detection 🔍

   • Use metrics like disparate impact and equal opportunity to identify disparities.
   • Example:

     from fairnessml import BiasDetector  
     detector = BiasDetector(model, sensitive_features='race')  
     results = detector.analyze()  
     

2. Fairness Constraints ⚖️

   • Incorporate constraints to enforce fairness during training.
   • Popular approaches: Reweighting, Adversarial debiasing, and Constraint-based learning.

3. Auditing Models 🧪

   • Regularly audit models using tools like Fairlearn or AIF360.

Implementation Steps

• Data Preprocessing

  • Normalize sensitive attributes (e.g., gender, ethnicity).
  • 

• Algorithm Modification

  • Apply fairness-aware algorithms (e.g., Fairness-aware Logistic Regression).
  • 

• Evaluation & Mitigation

  • Measure fairness using statistical parity or predictive parity.
  • 

Case Studies

• Loan Approval Systems 💳
  • Reducing bias against underrepresented groups.
• Recruitment Tools 🧑‍💼
  • Ensuring equal opportunity across demographics.

Further Reading

• Understanding Bias in Machine Learning
• Fairness-ML Implementation Guide