Capstone Project: Bias Detection in AI Systems
artificial-intelligence-ai.
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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