🛡️MediumAlignment & SafetyPREMIUM

Bias & Fairness in LLMs

Master the taxonomy of LLM biases, implementation of fairness metrics, and end-to-end mitigation strategies from data curation to RLHF.

What you'll master

Types of bias: selection, representation, measurement

Bias evaluation benchmarks (BBQ, WinoBias)

Pre-training vs post-training mitigation

Fairness metrics (Demographic Parity, Equalized Odds)

Intersectional bias challenges

Impossibility Theorem of Fairness

Counterfactual Data Augmentation

RLHF-induced bias (Sycophancy)

Toxicity Filtering trade-offs

Medium35 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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