Bias and Representational Harm in LLMs

Gallegos, Rossi, Barrow, Tanjim, Kim, Dernoncourt, Yu, Zhang, Ahmed (Computational Linguistics 2024, arXiv:2309.00770). Foundational 2024 survey distinguishing representational harms (stereotypes, erasure) from allocational harms (unequal resource distribution) and categorizing evaluation metrics as embedding-based, probability-based, or generated-text-based. 2024-2025 empirical: An et al. (PNAS Nexus, March 2025) measure intersectional gender x race bias across GPT-3.5 Turbo, GPT-4o, Gemini 1.5 Flash, Claude 3.5 Sonnet, Llama 3-70B on automated resume evaluation for 20 entry-level jobs. WinoIdentity (COLM 2025, arXiv:2508.07111) introduces uncertainty-based fairness evaluation for intersectional identities. Yu & Ananiadou 2025 identify gender neurons in MLP layers; Ahsan & Wallace 2025 use SAEs to reveal clinical racial bias; Zhou et al. 2024 (UniBias) manipulates attention heads for debiasing. Meta-critique (arXiv:2508.11067): 10-year literature disproportionately focuses on binary-gender bias.

Build 1 hr Python (stdlib, toy embedding-based bias probe)

Prerequisites: Phase 05 (word embeddings), Phase 18 · 01 (instruction following)

#Learning Objectives

Define representational vs allocational harm and give one example of each in an LLM deployment.
Name the three evaluation-metric categories from Gallegos et al. 2024 and describe one metric from each.
Describe intersectionality and why WinoIdentity's uncertainty-based fairness measurement addresses gaps in single-axis bias evaluation.
Describe two mechanistic-interpretability approaches to bias (gender neurons, SAE features, attention-head manipulation).

#Learning Objectives

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