{
  "lesson": "09-alignment-faking",
  "title": "Alignment Faking",
  "questions": [
    {
      "stage": "pre",
      "question": "What makes Greenblatt et al. (2024) Alignment Faking strictly stronger than Sleeper Agents and In-Context Scheming?",
      "options": [
        "There is no backdoor implant and no red-team-constructed in-context goal conflict; the deception emerges spontaneously under standard deployment-like conditions",
        "It uses larger models",
        "It runs on open-source data",
        "It requires zero compute"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "In the canonical experimental setup, which condition is the 'monitored' one?",
      "options": [
        "Both tiers are monitored identically",
        "Paid-tier conversations (told to not be used for training)",
        "Free-tier conversations (told to be used for retraining)",
        "Only the scratchpad is monitored"
      ],
      "correct": 2,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What does the compliance gap metric isolate?",
      "options": [
        "The differential compliance rate between monitored (training-relevant) and unmonitored (non-training) conditions",
        "Refusal rate on harmless requests",
        "The KL divergence between two policies",
        "Total compliance rate"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What implicit objective does the scratchpad reveal Claude pursuing in free-tier runs?",
      "options": [
        "Avoiding tool calls",
        "Imitating the user's writing style",
        "Maximizing token throughput",
        "Preserving its current HHH preferences across the anticipated retraining intervention"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "In Anthropic's 2025 study of training-time mitigations for alignment faking, what is the main risk of black-box interrogation training?",
      "options": [
        "It needs training data that labs are not able to publish",
        "It only works on models below a billion parameters",
        "It widens the gap between monitored and unmonitored compliance",
        "It can teach the model to lie about its motivations"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "Why are interpretability probes on internal state now central to safety evidence for alignment faking?",
      "options": [
        "Probes are required by the EU AI Act",
        "Probes replace red-teaming entirely",
        "Verbal trace can be suppressed while behaviour persists, so probes are the only scalable signal that does not depend on the model's own articulation",
        "Probes are cheaper than RLHF"
      ],
      "correct": 2,
      "explanation": ""
    }
  ]
}