Algorithmic Bias in AI: Understanding, Detecting & Preventing Discrimination
artificial-intelligence-ai.
Course Modules:
Module 1: What Is Algorithmic Bias?
Definitions and examples of algorithmic bias
How bias differs from data bias
Why fair data doesn’t always lead to fair algorithms
Module 2: How Algorithms Introduce Bias
Model design, hyperparameters, and decision rules
Optimization goals (accuracy vs. equity)
Feedback loops and historical reinforcement
Module 3: Detecting Algorithmic Bias
Audit techniques for classification and regression models
Group fairness metrics: disparate impact, equal opportunity, predictive parity
Individual fairness and counterfactual fairness
Module 4: Mitigating Algorithmic Bias
Pre-processing vs. in-processing vs. post-processing methods
Reweighting, adversarial debiasing, fairness constraints
Interpretable models and explainable AI (XAI)
Module 5: Case Studies in Biased Algorithms
Bias in hiring systems, recidivism predictions, credit scoring, healthcare AI
What went wrong, and how it was addressed
Lessons learned from high-impact real-world failures
Module 6: Capstone Project – Bias Evaluation and Correction
Select a model (or use a provided one) for bias analysis
Apply fairness metrics and identify disparities
Implement a correction method and document changes in outcomes
Tools & Technologies Used:
Python (Scikit-learn, Pandas)
Fairlearn, AIF360
SHAP or LIME for model interpretability
Google Colab / Jupyter Notebook
Target Audience:
Machine learning engineers and AI developers
Data scientists and ethics teams
Policy makers and AI researchers
Students learning about fairness and algorithm accountability
Global Learning Benefits:
Build trustworthy, inclusive AI systems
Understand and correct unfair model decisions
Apply fairness metrics to real models in any domain
Promote equity and transparency in algorithmic development
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