AI Ethics — Practical Fairness for Data Scientists

This repository contains the projects developed during the AI Ethics Specialization at Turing College. Each project corresponds to one of the program's modules and addresses distinct ethical challenges associated with the design, development, and deployment of artificial intelligence systems.

Objective

The primary goal is to translate abstract ethical principles into actionable, measurable practices for machine learning systems. Across the modules, this specialization builds a progression from conceptual auditing to production-ready fairness tooling:

• Module 1: How to detect and measure bias in AI systems.
• Module 2: How to intervene at the data and model level to reduce bias.
• Module 3: How to implement fairness at scale within an organization.
• Final Project: How to integrate all of the above into a single, automated, reproducible pipeline.

Repository Structure

AI-Ethics/
├── Module_1/          Fairness Audit Framework
├── Module_2/          Fairness Intervention Playbook
├── Module_3/          Fairness Implementation Playbook
└── Final_Project/     Fairness Pipeline Development Toolkit

Module 1 — Fairness Audit Framework

A comprehensive playbook for systematically evaluating AI systems for bias and fairness issues. Includes a glossary of fairness concepts, an executive summary for stakeholders, a technical audit report, and an implementation guide.

Module 2 — Fairness Intervention Playbook

Integrates four fairness intervention approaches — causal analysis, pre-processing, in-processing, and post-processing — into a unified workflow. Covers integration strategies, case studies, validation frameworks, intersectional fairness, and adaptability guidelines.

Module 3 — Fairness Implementation Playbook

An end-to-end methodology for deploying fairness systematically across AI systems and organizations. Designed for director-level stakeholders and cross-functional teams, it covers implementation, integration, case studies, validation, adaptability, and future iterations.

Final Project — Fairness Pipeline Development Toolkit

The capstone deliverable: a configuration-driven, automated fairness pipeline that integrates the measurement, data engineering, and model training modules from the previous three modules into a single orchestrated system.

Key components:

• config.yml — Declarative configuration defining the entire fairness workflow
• run_pipeline.py — Three-step orchestrator: Baseline Measurement, Transform & Train, Final Validation (PASS/FAIL)
• demo.ipynb — Interactive demonstration notebook
• MLflow integration — Full experiment traceability (metrics, model artifacts, configuration)
• 35 automated tests — Unit, functional, and end-to-end integration tests

For full documentation, see Final_Project/README.md.

Specialization Progression

graph LR
    M1["Module 1<br/><b>Audit</b><br/><small>Can we detect bias?</small>"]
    M2["Module 2<br/><b>Intervene</b><br/><small>Can we reduce bias?</small>"]
    M3["Module 3<br/><b>Implement</b><br/><small>Can we scale fairness?</small>"]
    FP["Final Project<br/><b>Integrate</b><br/><small>Can we automate it?</small>"]
    M1 --> M2 --> M3 --> FP

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Tech Stack

Technology	Used in	Purpose
Python	Final Project	Core implementation language
scikit-learn	Final Project	ML models, pipelines, calibration
Fairlearn	Final Project	Constrained fairness optimization
PyTorch	Final Project	Neural network fairness regularization
MLflow	Final Project	Experiment tracking and artifact management
Markdown + Mermaid	All modules	Documentation and architecture diagrams

About

Program: AI Ethics Specialization — Turing College Focus: Practical fairness for data scientists — bridging the gap between ethical principles and production ML systems.