Phase 2b: LLM-Specific Responsible AI · 45 min · Python · transformers · NLTK

LLM-Specific Bias & Representational Harm

A classifier that scores 90% accuracy can still erase an entire demographic from its output. Accuracy is not fairness.

Hiring signal: Candidates who can distinguish representational harm from allocative harm, and who have measured stereotype amplification in generated text rather than just citing it as a concept, show the practical RAI engineering skills that companies need.

What you will learn

Distinguish representational harm from allocative harm in LLM outputs
Measure stereotype amplification in generated text using StereoSet and CrowS-Pairs
Detect demographic erasure and under-representation in LLM completions
Measure sentiment disparity across demographic mentions in generated text
Identify bias in LLM-generated code, hiring text, and evaluations

Introduction

LLM-Specific Bias & Representational Harm

Why This Lesson Matters

Traditional ML bias is about allocative harm — who gets the loan, who gets the job, who gets parole. LLM bias is often about representational harm — how a group is depicted, stereotyped, or erased in generated text. These harms are harder to measure but equally damaging.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Representational vs. Allocative Harm, Stereotype Amplification, Demographic Erasure, Sentiment Disparity, Bias in LLM-Generated Code, Bias in LLM-Generated Evaluations, Key Takeaways — plus a hands-on lab, quiz, and project artifact.

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