Field
Safety & Ethics
What goes wrong, who it affects, and what actually helps.
This field is where the arguments are, and the entries don't pretend otherwise.
Some of it is technical. Jailbreaking keeps working because refusals are learned tendencies rather than rules — you're shifting a statistical balance, not bypassing a check. Prompt injection is unsolved in the general case, and that's structural: instructions and data share a channel. Red-teaming produces a map of reachable harm, not a certificate.
Some of it is unresolved and honest about it. Explainability has a problem most coverage skips: post-hoc explanations are reconstructions, and there's no ground truth to check them against. Bias and fairness has a proof that fairness definitions conflict mathematically — you cannot satisfy all of them. Privacy runs into the fact that models memorise and unlearning doesn't work.
Start with AI Alignment for the framing, or Guardrails if you're shipping something this week.
6 concepts in this field
AI Alignment
The problem of making AI systems actually do what people intend — reliably pursuing the goals we want, not just the ones we accidentally specified.
Bias & Fairness
The problem of AI systems producing unfair or discriminatory outcomes — usually by absorbing biases present in their training data.
Explainability
Getting a model to show its working — and the uncomfortable fact that most methods explain the explanation, not the decision.
Jailbreaking
Getting a model to do what it was trained to refuse — and the structural reason it keeps working.
Red-teaming
Attacking your own system on purpose, before someone else does it for free.
Privacy & PII
Personal data going into AI systems, coming back out of them, and the fact that a trained model is very hard to un-train.