Selection Bias in AI: How Skewed Sampling Skews Predictions
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Course Modules:
Module 1: What is Selection Bias?
Definitions and examples of selection bias in AI
Types: sampling bias, survivorship bias, non-response bias
Real-world impacts (e.g., loan approvals, hiring, healthcare models)
Module 2: How Selection Bias Affects Model Performance
Poor generalization and overfitting
Demographic exclusion and fairness issues
Case study: when biased models mislead decision-making
Module 3: Detecting Selection Bias in Datasets
Analyzing dataset distribution vs. real-world data
Using summary statistics, histograms, and visualizations
Identifying missing or underrepresented groups
Module 4: Strategies to Reduce Selection Bias
Data collection planning: representative and inclusive sampling
Augmenting underrepresented classes or demographics
Importance sampling, reweighting, and stratified sampling techniques
Module 5: Testing and Validation in the Presence of Bias
Creating balanced test sets
Fairness-aware cross-validation
Evaluation metrics beyond accuracy
Module 6: Capstone Project – Bias Detection and Correction
Choose or receive a skewed dataset (e.g., job applications, reviews)
Analyze for selection bias and document disparities
Apply at least one correction method and compare model outcomes
Tools & Technologies Used:
Python (Pandas, NumPy, Scikit-learn)
Fairlearn, AIF360 for fairness evaluation
Google Colab or Jupyter Notebooks
Matplotlib / Seaborn for visualization
Target Audience:
AI/ML developers and data scientists
Researchers and evaluators working with data
Policy and ethics teams ensuring model fairness
Students studying responsible AI development
Global Learning Benefits:
Build AI models that generalize across real-world populations
Avoid biased decisions caused by poor sampling
Increase model trust, transparency, and ethical compliance
Equip yourself with practical skills for fair AI pipeline design
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