This is an educational and exploratory tool. Alignment risk assessments are subjective and based on current AI safety research. Consult AI safety experts for serious risk assessment.
Alignment Risk Factors
0% = Narrow AI, 50% = Human-level AGI, 100% = Superintelligence
Model Capability Level
Current frontier models (GPT-4, Claude 3) are at ~20–30% on this scale. AGI (human-level across all domains) is the 50% mark. Superintelligence (vastly beyond human) is 100%. Higher capability increases both potential benefits and alignment difficulty.
0% = No alignment, 40% = RLHF, 70% = Constitutional AI, 100% = Full interpretability
Alignment Method Robustness
RLHF (Reinforcement Learning from Human Feedback) is the current industry standard but is known to be brittle. Constitutional AI and interpretability research are more robust but less mature. No method has been proven to scale to superhuman systems.
0% = No oversight, 30% = Human review, 70% = Automated oversight, 100% = Multi-layer oversight
Oversight Quality
Human review: slow but contextual. Automated oversight: scalable but may miss edge cases. Multi-layer: combines human + automated + formal verification. No oversight = unchecked deployment with maximum risk.
0% = Cautious phased rollout, 60% = Normal deployment, 100% = Rushed competitive deployment
Deployment Speed
Rushed deployment increases risk of undiscovered flaws. Cautious phased rollout allows iterative safety testing. Competitive pressure often forces faster deployment than safety would dictate — a key concern in AI races between companies and nations.
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Explore →Frequently Asked Questions
What is AI alignment? AI alignment is the challenge of ensuring that advanced AI systems pursue goals that are beneficial to humanity — even as they become more capable. It involves technical methods like RLHF, Constitutional AI, and interpretability research.
What factors increase alignment risk? Higher model capability, weaker alignment methods, inadequate oversight, and rushed deployment all increase alignment failure probability. The risk compounds when multiple factors are unfavorable.
What are the main alignment methods? Key methods include RLHF (Reinforcement Learning from Human Feedback), Constitutional AI (self-supervision based on principles), mechanistic interpretability (understanding model internals), and scalable oversight (using AI to supervise AI).
How accurate is this calculator? This is a simplified model for educational purposes. Real alignment risk depends on many factors not captured here, including specific model architectures, training data, and deployment context.
How the Risk Score Is Calculated
Risk Score Formula
Risk = w₁·Capability + w₂·(1−AlignmentMethod)
+ w₃·(1−Oversight) + w₄·DeploymentSpeedWeights derived from relative importance rankings in AI safety literature. Each factor scored 0–1; final score mapped to 0–100.
Alignment Method Effectiveness
RLHF alone: ~0.4 effectiveness
Constitutional AI: ~0.6 effectiveness
Interpretability: ~0.7 effectiveness
Scalable Oversight: ~0.8 effectiveness
Multi-layer combined: ~0.9 effectivenessEstimates based on Anthropic, DeepMind and ARC Evals published safety benchmarks (2023–2024).
Capability Thresholds
Narrow AI (task-specific): Low risk baseline
GPT-4 class (broad): Moderate risk
AGI-capable: High risk
Superintelligent: Critical riskBased on Bostrom (2014) capability taxonomy and Cotra (2021) "Forecasting TAI" timelines.
Authoritative Sources
- Bostrom, N. (2014): Superintelligence: Paths, Dangers, Strategies. Oxford University Press. Foundational analysis of AI alignment failure modes. → Author page
- Russell, S. (2019): Human Compatible: Artificial Intelligence and the Problem of Control. Viking. Inverse Reward Design framework and cooperative AI principles.
- Anthropic (2022): "Constitutional AI: Harmlessness from AI Feedback." Introduces self-supervision via explicit principles as an alignment method. → arXiv
- Christiano, P., et al. (2017): "Deep Reinforcement Learning from Human Preferences." Foundational RLHF paper. NeurIPS 2017. → arXiv
- Cotra, A. (2021): "Why AI alignment could be hard with modern deep learning." Alignment Forum. Risk scaling with capability. → Alignment Forum
- ARC Evals (2023): Model evaluation reports for GPT-4 and Claude 2 on dangerous capability benchmarks. → ARC Evals
- AI Safety Institute (UK, 2024): Advanced AI Evaluations Framework. Government-level alignment oversight methodology. → AISI
"The development of full artificial intelligence could spell the end of the human race… It would take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded."
"The key question for AI safety is not whether AI systems will be powerful, but whether they will be aligned with human values and subject to meaningful human oversight as they become more capable."