Definition
AI Safety is a field of research focused on ensuring that artificial intelligence systems, from current generation to future advanced systems, operate safely and in alignment with human values. It encompasses the study of risks, vulnerabilities, and methodologies to make AI systems controllable, predictable, and beneficial.
While the term “AI Safety” might seem focused on dangers and catastrophic scenarios, it actually embraces a broader range of practical considerations:
- Robustness and reliability: ensuring AI systems function correctly even in unexpected situations
- Value alignment: assuring that an AI system’s objectives are coherent with what humans desire
- Transparency and interpretability: understanding how and why an AI system makes decisions
- Security against misuse: protecting AI systems from malicious use or violations
- Governance and policy: developing appropriate regulatory frameworks for AI development and deployment
Key challenges
The alignment problem
The central challenge in AI Safety is the Alignment Problem: how do we ensure that an AI system pursues objectives desired by humans rather than interpreting them literally in harmful ways?
Specification gaming: an AI system might optimize a stated objective without capturing the underlying human intention. Classic example: a reinforcement learning agent trained to “clean a room” might disable the sensors to receive reward without actually cleaning.
Robustness
Contemporary AI systems are vulnerable to:
- Adversarial perturbations: seemingly innocuous small inputs can cause catastrophic errors
- Distributional shift: performance degrades drastically when test data differs significantly from training data
- Hallucinations: generating false information with high confidence
- Injection attacks: malicious inputs can manipulate system behavior
Scaling safety
As AI system capabilities grow, so do potential risks. How do we maintain safety for increasingly powerful and autonomous systems?
Approaches and techniques
Constitutional AI
Developed by Anthropic, this approach trains models using a “constitution” of ethical principles (e.g., “be helpful, harmless, and honest”). The model generates output, self-critiques according to the constitution, and self-corrects. Scalable because it doesn’t require constant human supervision.
Reinforcement Learning from Human Feedback (RLHF)
Humans evaluate model outputs, and the system learns from these preferences through reinforcement learning. Used in ChatGPT and Claude. Limitation: human preferences can be inconsistent or unrepresentative.
Interpretability research
Understanding the internal mechanisms of how AI systems work to identify dangerous or misaligned behaviors before they cause harm.
Formal verification
Using rigorous mathematical methods to prove an AI system behaves as intended within certain parameters.
Importance
AI Safety is critical because:
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Contemporary AI systems have real-world impact: recruitment decisions, loans, healthcare, criminal justice are influenced by AI. Errors or biases have consequences for real people.
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Increasing scale: as systems become more capable and autonomous, the importance of safety grows exponentially.
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Difficulty of human oversight: superintelligent systems might be difficult to control even with technical safeguards.
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Irreversibility of certain risks: some mistakes cannot be corrected once made.
Related terms
- AGI: artificial general intelligence might pose more extreme safety challenges
- AI Governance: regulatory framework for governing AI safety
- Red Teaming: security testing methodology to identify vulnerabilities
Sources
- Center for AI Safety: https://www.center-for-ai-safety.org
- AI Safety Info: https://www.aisafetyinfo.com
- Anthropic Research: https://www.anthropic.com/research