AI Safety
Definition
AI safety is the field focused on ensuring AI systems behave as intended, avoid harmful outputs, resist manipulation, and remain under human control as they become more capable.
Why It Matters
As AI systems become more powerful and autonomous, ensuring they behave safely becomes critical. AI safety encompasses preventing immediate harms (toxic outputs, privacy violations) and longer-term concerns (misalignment, loss of control). For AI engineers, safety isn’t optional - it’s a core responsibility.
Key Areas
Near-term Safety:
- Preventing harmful outputs (toxicity, bias)
- Input validation and prompt injection defense
- Privacy protection and data handling
- Reliable error handling and fallbacks
Long-term Safety:
- Alignment (AI goals match human intent)
- Interpretability (understanding AI decisions)
- Robustness (behaving well in edge cases)
- Control (maintaining human oversight)
Practical Implementation
Start with: content filtering on outputs, input sanitization, rate limiting, logging and monitoring, human review for high-stakes actions, and clear escalation paths. Advanced measures include red teaming, formal verification, and alignment techniques.