P-Doom Measure

A P-Doom Measure is an AI risk probability measure that is an existential life-risk assessment measure.

• AKA: Probability of Doom, P(Doom), AI Doom Probability.
• Context:
  • It can typically express Individual AI Risk Assessments through percentage values ranging from 0% to 100%.
  • It can typically capture Existential Risk Beliefs about artificial general intelligence development timelines.
  • It can typically inform AI Safety Research Prioritys through risk quantification frameworks.
  • It can typically facilitate AI Risk Communication between AI safety researchers and policy makers.
  • It can typically vary based on AGI Timeline Assumptions and AI capability trajectory beliefs.
  • ...
  • It can often reflect Personal Epistemic Uncertainty about AI development pathways.
  • It can often influence AI Governance Decisions through risk perception metrics.
  • It can often serve as Community Coordination Signals within AI safety communitys.
  • It can often incorporate Multiple Risk Factors including AI misalignment risks and AI capability risks.
  • ...
  • It can range from being a Low P-Doom Measure to being a High P-Doom Measure, depending on its AI risk assessment value.
  • It can range from being a Near-Term P-Doom Measure to being a Long-Term P-Doom Measure, depending on its AI timeline scope.
  • ...
  • It can be derived from AI Safety Expert Surveys for consensus assessments.
  • It can be updated with New AI Capability Evidence for dynamic risk tracking.
  • It can be compared across Different AI Safety Organizations for perspective analysis.
  • It can be incorporated into AI Policy Frameworks for regulatory decision support.
  • It can be communicated through Public AI Safety Discussions for awareness building.
  • ...
• Example(s):
  • Individual P-Doom Measures, such as:
    • AI Researcher P-Doom Measures, such as:
      • Eliezer Yudkowsky P-Doom Measure (reported as >90%), expressing high existential concern.
      • Stuart Russell P-Doom Measure, reflecting moderate AI risk assessment.
    • AI Organization P-Doom Measures, such as:
      • MIRI P-Doom Measure, representing organizational risk consensus.
      • DeepMind Safety Team P-Doom Measure, indicating industry safety perspective.
  • Survey-Based P-Doom Measures, such as:
    • AI Impacts Survey P-Doom Measure (2022), aggregating expert opinions.
    • FHI Survey P-Doom Measure, compiling academic risk assessments.
  • Conditional P-Doom Measures, such as:
    • P-Doom Given No AI Alignment Progress, assessing worst-case scenarios.
    • P-Doom Given Current Trajectory, evaluating status quo risks.
  • ...
• Counter-Example(s):
  • General AI Risk Measures, which lack specific doom probability quantification.
  • AI Capability Predictions, which focus on performance milestones rather than existential risk probability.
  • AI Timeline Estimates, which predict AGI arrival dates without catastrophic outcome probability.
• See: Humanitarian Measure, AI Existential Risk, AI Safety Research, AGI Timeline, AI Alignment Problem.

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