{
  "lesson": "20-bias-representational-harm",
  "title": "Bias and Representational Harm in LLMs",
  "questions": [
    {
      "stage": "pre",
      "question": "How does the lesson distinguish representational harm from allocational harm?",
      "options": [
        "Representational is computational; allocational is statistical",
        "Allocational harm only occurs offline",
        "Representational harm only matters for vision models",
        "Representational harm is stereotypes / erasure / demeaning portrayal; allocational harm is unequal material outcomes for a group"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "Which of these are the three evaluation-metric categories from Gallegos et al. 2024?",
      "options": [
        "Vision, audio, text",
        "WEAT, BLEU, ROUGE",
        "Embedding-based, probability-based, generated-text-based",
        "Pre-RLHF, mid-RLHF, post-RLHF"
      ],
      "correct": 2,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "Why is intersectional fairness evaluation needed in addition to single-axis evaluation?",
      "options": [
        "Single-axis measures are computationally cheaper",
        "Intersectionality is the same as cross-validation",
        "Single-axis is the only legally allowed approach",
        "Bias that only emerges at intersections (e.g., gender x race) is missed by evaluations that look at one axis at a time"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What does WinoIdentity (COLM 2025) introduce?",
      "options": [
        "Uncertainty-based intersectional fairness evaluation that compares the model's uncertainty over outcomes across intersectional identity tuples, not just point predictions",
        "An adversarial attack on demographic classifiers",
        "A replacement for WEAT in embedding space",
        "A new tokenizer for identity terms"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "Which of these is a mechanistic-interpretability approach to bias?",
      "options": [
        "Re-labeling the training set",
        "Identifying gender neurons in MLP layers, using SAEs to surface race-correlated features, or manipulating specific attention heads (UniBias)",
        "Switching to DPO from PPO",
        "Adding more KL regularization"
      ],
      "correct": 1,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "What does the 10-year meta-critique (arXiv:2508.11067) argue about the bias literature?",
      "options": [
        "It disproportionately focuses on binary-gender bias, neglecting other axes (disability, religion, migration status, multi-lingual identity) that may harm marginalized groups by neglect",
        "It is balanced across all identity axes",
        "It avoids quantitative methods",
        "It overemphasizes intersectionality"
      ],
      "correct": 0,
      "explanation": ""
    }
  ]
}