Master Study AI

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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