Technical AI Safety

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LLM Summary:This page contains only code/component references with no actual content about technical AI safety. The page is a stub that imports React components but provides no information, analysis, or substance.

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See also:80,000 HoursEA ForumAI Safety Info

Research and engineering practices aimed at ensuring AI systems reliably pursue intended goals. Core challenges include goal misgeneralization (60-80% of RL agents exhibit this in distribution-shifted environments) and supervising systems that may exceed human capabilities.

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What Drives AI Safety Adequacy?

Causal factors affecting technical AI safety outcomes. The field faces a widening gap: alignment methods show brittleness, interpretability is progressing but incomplete, and evaluation benchmarks are unreliable.

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Derived

Direct Factors

Target

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Scenarios Influenced

Scenario	Effect	Strength
AI Takeover	↑ Increases	strong
Human-Caused Catastrophe	↑ Increases	weak
Long-term Lock-in	↑ Increases	medium

Related Content

Related Risks

• Deceptive Alignment
• Scheming
• Goal Misgeneralization

Related Responses

• Interpretability
• Scalable Oversight

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What links here

• Intervention Portfolioapproach
• Field Building Analysisapproach