AI Safety & Alignment ️
Ensuring AI systems do what we actually want — safely and reliably
What is AI Safety?
AI Safety is about building AI systems that:
- Do what we want (alignment)
- Don't cause harm (safety)
- Remain under human control (controllability)
Why It Matters
As AI systems become more capable, the stakes get higher:
| Capability | Low-Risk Example | High-Risk Example |
|---|---|---|
| Text generation | Autocomplete | Disinformation at scale |
| Image recognition | Photo tagging | Surveillance abuse |
| Decision making | Movie recommendations | Autonomous weapons |
The Core Problems
1. The Alignment Problem Getting AI to do what we actually mean, not just what we literally say.
"Maximize paperclips" → AI converts all matter into paperclips (We meant: "Make a reasonable number of paperclips")
2. The Specification Problem It's hard to fully specify what we want. Humans have complex, context-dependent values.
3. The Control Problem As AI gets smarter, how do we ensure it stays controllable?
Current Safety Approaches
Guardrails: Rules and filters that prevent harmful outputs
User: "How do I make a bomb?"
AI: "I can't help with that."
RLHF: Training AI with human feedback to prefer helpful, harmless responses
Red-teaming: Deliberately trying to break systems to find vulnerabilities
Monitoring: Tracking AI behavior for unexpected patterns
What You Can Do
- Be skeptical of AI outputs — they can be wrong or biased
- Report issues when you see AI behaving badly
- Stay informed about AI capabilities and limitations
- Think critically about how AI is deployed in your world
References
Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking Press.
Hendrycks, D., Mazeika, M., & Woodside, T. (2021). An Overview of Catastrophic AI Risks. arXiv preprint.
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete Problems in AI Safety. arXiv preprint.
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., et al. (2022). Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. arXiv preprint.
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep Reinforcement Learning from Human Preferences. Advances in Neural Information Processing Systems (NeurIPS).
Citation Note: All referenced papers are open access. We encourage readers to explore the original research for deeper understanding. If you notice any citation errors, please let us know.