📊 Data Science Week 1 of 14 BSc · Y3 S2 ⏱ ~50 min

Week 1: Algorithmic Bias, Fairness & Privacy

Examine algorithmic bias, data privacy regulations, accountability frameworks, and the ethical responsibilities of data scientists in society.

University of Aliens

DS306 — Lecture 1 · BSc Y3 S2

🎬 CC Licensed Lecture

0:00 / —:—— 📺 Creative Commons Licensed

🎯 Learning Objectives

Identify sources of bias in data collection and model training
Apply fairness metrics (demographic parity, equalized odds)
Understand GDPR, CCPA, and data privacy regulations
Construct an AI ethics impact assessment

Topics Covered This Lecture

Algorithmic Bias & Fairness Metrics

Data Privacy & Regulation (GDPR/CCPA)

Explainability & Model Transparency

Responsible AI Frameworks

📖 Lecture Overview

This first lecture establishes the foundational framework for Ethics in Data Science. By the end of this session, you will have the conceptual grounding and practical starting point needed for the rest of the course.

        Why this matters
        Examine algorithmic bias, data privacy regulations, accountability frameworks, and the ethical responsibilities of data scientists in society. This lecture sets up everything that follows — make sure you understand the core concepts before proceeding to Week 2.
      

Key Concepts

The lecture introduces the four main pillars of this course: Algorithmic Bias & Fairness Metrics, Data Privacy & Regulation (GDPR/CCPA), Explainability & Model Transparency, Responsible AI Frameworks. Each will be explored in depth over the 14-week curriculum, with hands-on projects reinforcing theory at every stage.

# Quick Start: verify your environment is ready for DS306
import sys
print(f"Python {sys.version}")

# Check key libraries are installed
try:
    import numpy, pandas, matplotlib
    print("✅ Core libraries ready")
except ImportError as e:
    print(f"❌ Missing: {e} — run: pip install numpy pandas matplotlib")

This Week's Focus

Focus on mastering: Algorithmic Bias & Fairness Metrics and Data Privacy & Regulation (GDPR/CCPA). These are the prerequisites for everything in Week 2. The concepts build on each other — do not skip the practice exercises.

📋 Project 1 of 3 50% of Final Grade

DS306 Project 1: Bias Audit of a Predictive Model

Audit a publicly available predictive model (e.g., COMPAS recidivism, loan approval) for demographic bias. Apply fairness metrics and propose bias mitigation strategies.

Bias audit report with statistical evidence
Fairness metric calculations (3+ metrics)
Mitigation strategy with before/after comparison
Policy recommendation for responsible deployment

50%

3 Projects

20%

Midterm Exam

30%

Final Exam

📝 Sample Exam Questions

These represent the style and difficulty of questions you'll see on the midterm and final. Start thinking about them now.

Conceptual Short Answer

Define demographic parity and equalized odds as fairness metrics. Can both be satisfied simultaneously?

Analysis Short Answer

What is the difference between explainability and interpretability in AI models?

Applied Code / Proof

Name three data collection practices that can introduce bias into a machine learning dataset.