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Bias & Fairness in AI

Discover how bias sneaks into AI systems and learn practical ways to build fairer, more inclusive technology. You'll audit real datasets and create your own fairness report!

20 modules·Difficulty: ★★★☆☆· 5 Free
Start module 1

Modules

  • 1
    Free8 min
    What Is Bias?
    Meet bias—the invisible patterns that make AI treat people differently, and learn why fairness matters in tech! 🎯
  • 2
    Free9 min
    Where Does Bias Hide?
    Explore the sneaky places bias hides—from data collection to algorithm design—with real examples from everyday AI.
  • 3
    Free10 min
    Historical Bias Uncovered
    Discover how past unfairness gets baked into training data and why old patterns can create new problems.
  • 4
    Free11 min
    Representation Matters
    Learn why having diverse voices in your dataset is crucial—and what happens when groups are left out.
  • 5
    Free10 min
    Measurement Bias Basics
    Understand how the way we measure and label data can introduce hidden unfairness into AI systems.
  • 6
    Paid12 min
    Your First Data Audit
    Roll up your sleeves and inspect a mini dataset to spot imbalances and missing groups using simple counting techniques.
  • 7
    Paid11 min
    Visualizing Unfairness
    Turn numbers into colorful charts and graphs that reveal hidden patterns and make bias visible to everyone.
  • 8
    Paid10 min
    Fairness Metrics 101
    Meet the measuring tools that help us score how fair an AI system is—like demographic parity and equal opportunity.
  • 9
    Paid9 min
    Balancing Your Dataset
    Practice techniques like oversampling and undersampling to fix lopsided data and give every group a fair shot.
  • 10
    Paid11 min
    Label Review Workshop
    Examine how labels can carry hidden assumptions and learn to write fairer, more accurate category names.
  • 11
    Paid12 min
    Algorithm Fairness Tweaks
    Dive into the code and adjust algorithm settings to prioritize fairness alongside accuracy in predictions.
  • 12
    Paid10 min
    Testing for Disparate Impact
    Run experiments to see if your AI treats different groups differently—even when it looks neutral on the surface.
  • 13
    Paid11 min
    Debiasing Techniques Showdown
    Compare pre-processing, in-processing, and post-processing methods to reduce bias at different pipeline stages.
  • 14
    Paid10 min
    Human-in-the-Loop Fairness
    Explore how keeping humans involved in AI decisions can catch mistakes and add empathy to automated systems.
  • 15
    Paid12 min
    Case Study: Real-World Bias Fixes
    Analyze how organizations identified and corrected bias in hiring tools, loan systems, and image recognition apps.
  • 16
    Paid11 min
    Building Your Fairness Checklist
    Create a step-by-step checklist that you can use to audit any AI project for fairness from start to finish.
  • 17
    Paid12 min
    Documenting Bias Findings
    Learn to write clear reports that explain what bias you found, why it matters, and how you plan to fix it.
  • 18
    Paid10 min
    Presenting Fairness Recommendations
    Practice communicating your fairness insights to teammates and decision-makers using visuals and simple language.
  • 19
    Paid12 min
    Capstone: Your Fairness Report
    Pull together everything you've learned to audit a mini dataset and produce a complete fairness report with findings and fixes.
  • 20
    Final exam15 min
    Final Exam: Bias & Fairness Mastery
    Show what you know about spotting bias, auditing data, and building fairer AI—your fairness journey ends here!