Master Study AI

Capstone Project: Bias Detection in AI Systems

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 Course Modules:

 Module 1: Project Setup and Dataset Selection

Choose a domain: hiring, healthcare, finance, or justice

Identify protected attributes (e.g., gender, race, age)

Define fairness goals and auditing questions

 

 Module 2: Exploratory Analysis by Demographic

Segment data by sensitive attributes

Visualize distributions and label imbalance

Identify disparities in outcomes or features

 

 Module 3: Apply Fairness Metrics and Statistical Tests

Use metrics: demographic parity, equal opportunity, predictive parity

Conduct tests: chi-square, KS test, PSI

Use tools: AIF360, Fairlearn, or manual calculations

 

 Module 4: Model Performance Disparity Analysis

Evaluate performance metrics across subgroups

Compare precision, recall, F1 by group

Visualize disparities with confusion matrices and fairness dashboards

 

 Module 5: Recommendations and Remediation Strategies

Suggest changes to data collection, model tuning, or thresholding

Evaluate potential trade-offs (accuracy vs. fairness)

Document limitations and ethical concerns

 

 Module 6: Final Report and Presentation

Submit a full audit report including visuals, metrics, and analysis

Prepare a stakeholder-friendly presentation (slides or video)

Include code appendix and reproducibility instructions

 

 Tools & Technologies Used:

Python (Pandas, Seaborn, SciPy, Scikit-learn)

Fairlearn, AIF360, SHAP (optional for explainability)

Jupyter Notebook or Google Colab

Visualization tools: Matplotlib, Plotly, Power BI (optional)

 

 Target Audience:

Advanced AI learners focused on responsible development

Students completing ethics or fairness tracks

ML engineers building inclusive models

Analysts preparing for roles in AI regulation or QA

 

 Global Learning Benefits:

Showcase your ability to detect and document AI bias

Apply industry-standard fairness metrics and frameworks

Build a portfolio-ready project aligned with responsible AI values

Strengthen your readiness for AI ethics, compliance, and audit roles

 

 

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