Tools & Methods to Detect and Reduce Bias in AI Systems
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Course Modules:
Module 1: Introduction to Bias in AI Systems
Types of bias (historical, label, selection, algorithmic)
Why technical fixes alone aren’t enough
Overview of the AI fairness lifecycle
Module 2: Measuring Fairness with Metrics
Group fairness: demographic parity, equal opportunity, predictive equality
Individual fairness and counterfactual fairness
When and how to apply fairness metrics
Module 3: Bias Detection Tools and Libraries
IBM’s AIF360 toolkit
Fairlearn for model evaluation and dashboards
SHAP & LIME for interpretability and feature impact analysis
Module 4: Preprocessing Bias Mitigation
Reweighing, oversampling, undersampling
Removing bias proxies and repairing datasets
Using pipelines to clean and balance data
Module 5: In-Processing and Post-Processing Techniques
Fairness-aware training (e.g., adversarial debiasing)
Adding constraints to model optimization
Equalizing outcomes with calibrated outputs
Module 6: Capstone Project – Bias Mitigation Workflow
Choose a biased dataset (e.g., income prediction, recidivism, resume screening)
Audit for bias using fairness metrics and tools
Apply at least one mitigation technique and report outcomes
Tools & Technologies Used:
Python, Scikit-learn, Pandas
Fairlearn, AIF360, SHAP, LIME
Google Colab or Jupyter Notebooks
Optional: DVC for tracking model fairness over time
Target Audience:
Data scientists and ML engineers
AI ethics researchers and QA teams
Developers building socially responsible AI
Students and professionals in AI governance
Global Learning Benefits:
Detect and correct hidden bias in AI models and datasets
Improve performance across underrepresented groups
Comply with fairness standards and responsible AI guidelines
Build AI that earns trust and performs fairly in the real world
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