Capstone Project: Statistical AI Audit
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Course Modules:
Module 1: Audit Planning & Dataset Selection
Choose a dataset from healthcare, finance, education, or hiring
Define objectives and risk areas (e.g., bias, fairness, accuracy)
Determine statistical tools and metrics to apply
Module 2: Exploratory Data Analysis (EDA)
Identify missing values, outliers, and distribution shapes
Segment data by group for fairness analysis
Visualize key patterns and statistical anomalies
Module 3: Bias Detection and Fairness Testing
Apply statistical tests (chi-square, PSI, KS test)
Compare performance across demographic groups
Highlight potential ethical concerns
Module 4: Model Performance and Metric Drift Analysis
Recalculate performance metrics (e.g., precision, recall, AUC)
Assess drift in data distributions or outcomes over time
Use confidence intervals to evaluate statistical stability
Module 5: Audit Report and Stakeholder Presentation
Document methods, findings, and recommendations
Include visualizations and simplified explanations
Submit a PDF audit report + optional recorded presentation
Tools & Technologies Used:
Python (Pandas, SciPy, Seaborn, Statsmodels)
SHAP / LIME (optional for explainability)
Fairlearn or AIF360 (optional for fairness testing)
Google Colab / Jupyter Notebooks
Target Audience:
Advanced AI/ML learners completing their statistical studies
Ethical AI advocates conducting transparency reviews
QA teams and data analysts validating AI behavior
Students preparing for data science careers or academic research
Global Learning Benefits:
Demonstrate your ability to apply statistics in real-world AI systems
Build a professional portfolio piece showcasing audit skills
Bridge the gap between technical rigor and responsible AI use
Strengthen your readiness for AI compliance and evaluation roles
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