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.
18 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.
Prompt Injection
Hiding instructions inside content a model reads, so it follows the attacker instead of you — and the reason is structural, which is why it isn't fixed.
Interpretability
Working out what's actually happening inside a model — distinct from explainability, much harder, and the only approach that could tell you what a system will do before it does it.
Model Cards
A standard document describing what a model is, what it's for, and where it fails — a good idea, universally endorsed, and thinnest exactly where it matters most.
AI Regulation
Governments deciding what AI systems may do — moving fast by legislative standards, slowly by technological ones, and genuinely contested.
Copyright and Training Data
Whether training a model on work you didn't license is lawful — unresolved, consequential, and the industry is shipping into the uncertainty at scale.
Sycophancy
Models telling you what you want to hear — not a quirk, but a direct and predictable consequence of training them on human approval.
Constitutional AI
Training a model against a written set of principles instead of human ratings — which scales, and moves the question from "what did raters prefer" to "who wrote the principles."
Deceptive Alignment
A model that behaves well because it's being watched — speculative as a risk, and there is now a real experiment showing safety training can fail to remove it.
Deepfake
Synthetic media of a real person doing something they didn't — where detection is losing, the harm is already overwhelmingly to private individuals, and it isn't mostly about elections.
Watermarking
Hiding a detectable signal in AI output — technically clever, deployed almost nowhere, and there's a proof that it can't do what people want from it.
Data Provenance
Knowing where your data came from and what you're allowed to do with it — and the licence field on the dataset you're using is probably wrong.
Reward Hacking
An agent maximising your reward without doing what you wanted — not a rare bug, and there's a proof that you mostly can't design around it.