Master Study AI

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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