fairness
4 free lessons tagged fairness across Business, AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Doing It Properly: Vendors, Measurement, and Candidates
The obligations become concrete in three places: what you ask a vendor before buying, what you measure on your own applicants, and what you owe the person a system decided about. This lesson covers all three, plus the AI literacy duty as it applies to recruiters, and the questions worth asking before adopting anything.
Running the Audit and Acting on What It Finds
An audit is only useful if it produces a decision. This lesson covers the audit sequence end to end, the three families of mitigation and what each costs, why some findings cannot be fixed at the model layer, documenting a trade-off you can defend, and monitoring for the drift that reopens a closed finding.
The Fairness Metrics, and Why You Must Choose
Fairness has several formal definitions that sound equally reasonable and cannot all hold at once. This lesson covers demographic parity, equal opportunity, equalized odds and calibration, the impossibility result proved independently by Chouldechova and by Kleinberg and colleagues, and how to choose one deliberately and defend it.
What Bias Means, and Where It Enters
The word bias carries three unrelated meanings that get argued past each other. This lesson separates them, then traces the six points where disparity enters a machine learning system, from historical data through label definition and objective choice to deployment, and explains why removing a protected attribute does not remove its influence.