Capstone Project: Bias Detection in AI Systems

web-development.

 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

 

 

?Master Study NLP Fundamentals: The Foundation of Language Understanding in AI

?Shop our library of over one million titles and learn anytime

?‍? Learn with our expert tutors 

Read Also About Generative AI and Prompt Engineering