ai-engineering-from-scratch/phases/18-ethics-safety-alignment/26-model-system-dataset-cards/quiz.json at main · Amjad-AbuRmileh/ai-engineering-from-scratch · GitHub

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{
  "lesson": "26-model-system-dataset-cards",
  "title": "Model, System, and Dataset Cards",
  "questions": [
    {
      "stage": "pre",
      "question": "Which best describes the original Mitchell et al. (2019) Model Card?",
      "options": [
        "An OpenAPI specification",
        "A 'nutrition label' for ML models: training data, intended use, factors, metrics, evaluation data, quantitative disaggregated analyses, ethical considerations, caveats",
        "A model checkpoint manifest only",
        "A regulatory legal contract"
      ],
      "correct": 1,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What did Oreamuno et al. (2023) find about ethical-considerations adoption in Hugging Face model cards?",
      "options": [
        "Approximately 0.3% document ethical considerations",
        "Approximately 80% document ethical considerations",
        "No model card has any text at all",
        "All model cards document them"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What does Pushkarna et al. (Google 2022) Data Cards' telescopic / periscopic / microscopic layering provide?",
      "options": [
        "A boundary-object framing where the same document serves different readers (non-experts, ML practitioners, auditors) via three zoom levels of detail",
        "A pre-RLHF audit checklist",
        "A telescope-themed visualization library",
        "A privacy-preserving query interface"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What scope does a System Card cover that Model Cards do not?",
      "options": [
        "End-to-end AI system: model + safety stack + deployment context, including security capabilities, prompt-injection protection, data-exfiltration detection, alignment, and incident response",
        "Only the dataset",
        "Only the prompt template",
        "Only the tokenizer"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "What did Liang et al. (2024) report about model-card detail and Hugging Face download rate?",
      "options": [
        "Detailed model cards drive a +29.0% weekly download lift on HF for the treatment group vs control (diff-in-diff); adoption pressure is now market-driven, not only compliance-driven",
        "Only ethical-considerations sections affected downloads",
        "Detail had no effect on downloads",
        "Detail decreased download rates"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "What does Laminator (Duddu et al. 2024) contribute to documentation?",
      "options": [
        "An automated benchmarking harness",
        "Verifiable attestations via hardware TEE / cryptographic signatures, so the card can carry a proof-of-claim instead of just a claim",
        "A replacement for the Datasheets standard",
        "An LLM-based summary generator"
      ],
      "correct": 1,
      "explanation": ""
    }
  ]
}