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.

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

Objective

Evaluate an LLM as a sociotechnical system across factuality, toxicity, alignment, representation, and adversarial use.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

Create a free account — the opening phases of 29 of 35 courses are free, no credit card.