Algorithmic Bias in AI: Understanding, Detecting & Preventing Discrimination

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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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