Technical AI Safety

Technical AI safety encompasses research and engineering practices aimed at ensuring AI systems reliably pursue intended goals. The field has grown from a niche academic concern to critical research, driven by evidence that advanced systems may develop deceptive alignment or engage in scheming.

Core challenges include goal misgeneralization (60-80% of RL agents exhibit this in distribution-shifted environments) and the difficulty of supervising systems that may exceed human capabilities. Key approaches include interpretability research, scalable oversight, and AI control methodologies that constrain systems regardless of internal alignment.

Metric	Score	Notes
Changeability	45	Moderately tractable; may require fundamental breakthroughs
X-risk Impact	85	Primary defense against catastrophic misalignment
Trajectory Impact	70	Alignment quality shapes long-term AI future
Uncertainty	60	Uncertain which approaches will work

Related Content

Risks:

• Deceptive Alignment
• Scheming
• Goal Misgeneralization
• Corrigibility Failure

Responses:

• Interpretability Research
• Scalable Oversight
• AI Evaluations
• Technical Alignment

Models:

• Alignment Robustness Trajectory
• Scheming Likelihood Model

Key Debates:

• How hard is alignment? Scaling current techniques vs. requiring fundamental breakthroughs?
• Can interpretability scale to frontier models, or will they remain opaque?
• Can human oversight scale to superintelligent systems?

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Ratings

Metric	Score	Interpretation
Changeability	45/100	Somewhat influenceable
X-risk Impact	85/100	Substantial extinction risk
Trajectory Impact	70/100	Major effect on long-term welfare
Uncertainty	60/100	Moderate uncertainty in estimates