Additional resources

Instruction and application

In Progress

Dive deeper: additional learning materials

If you're interested, use the following resources to continue exploring topics related to this unit.

• Fairness metrics & audit workflows: Explore Fairlearn’s tutorials on computing and visualizing per-group metrics (FNR, FPR) and running bias audits in Python.
• Proxy derivation & slicing: Follow IBM AI Fairness 360’s notebook on deriving demographic proxies and creating disaggregated cohorts.
• Output calibration: Read “On Calibration of Modern Neural Networks” by Guo et al. and try the accompanying temperature-scaling recipes in scikit-learn.
• Instance re-weighting strategies: Browse AIF360’s re-weighing examples to learn how to tune sample weights for fairness without retraining from scratch.