AI Safety
Definition
AI safety encompasses practices, research, and engineering to ensure AI systems behave as intended, avoid harmful outputs, and remain under human control, spanning from immediate content filtering to long-term alignment.
Why It Matters
AI safety isn’t just a research concern for future superintelligence. It’s a practical requirement for every deployed AI system. Your chatbot shouldn’t give medical advice that harms users. Your code assistant shouldn’t generate malware. Your customer service agent shouldn’t leak confidential information.
The stakes increase as AI systems become more capable and autonomous. An AI agent that can execute code, send emails, or make purchases needs safety mechanisms to prevent misuse, whether from prompt injection attacks or model errors.
For AI engineers, safety is a professional responsibility. You’re building systems that interact with real people. Harmful outputs damage users and destroy trust. Safety engineering isn’t a nice-to-have; it’s table stakes for production AI.
Implementation Basics
Layers of AI Safety
1. Model-Level Safety Safety training during model development. RLHF and Constitutional AI teach models to refuse harmful requests. This is what API providers do before you access the model.
2. Application-Level Safety Your responsibility as an AI engineer:
- Input validation and filtering
- Output moderation and blocking
- Usage policies and rate limiting
- Logging and monitoring
3. System-Level Safety Architectural decisions that limit harm:
- Least privilege: models can only do what’s necessary
- Human-in-the-loop for high-stakes actions
- Graceful degradation when safety is uncertain
Practical Safety Measures
Content Filtering Classify inputs and outputs for harmful content. Block or flag violations. Use specialized classifiers (OpenAI Moderation, Perspective API) alongside model-level safety.
Guardrails Explicit rules that override model behavior. If output contains PII, redact it. If user asks for dangerous content, refuse regardless of model response.
Rate Limiting Prevent abuse by limiting request volume. Makes large-scale attacks impractical.
Monitoring Track safety-relevant metrics: refusal rates, flagged content, user reports. Detect emerging issues before they scale.
Incident Response Have a plan for safety failures. How do you roll back? Communicate with affected users? Document and prevent recurrence?
Safety is ongoing work, not a checkbox. As your system evolves and users discover new edge cases, your safety measures must evolve too.
Source
Concrete Problems in AI Safety identifies practical near-term safety challenges including distributional shift, reward hacking, and safe exploration that affect deployed systems.
https://arxiv.org/abs/2210.01790