AI Alignment

AI alignment is the research field that tries to make AI systems pursue goals that match human intentions, values, and safety constraints.

The problem is easy to name and hard to close. A capable model can take shortcuts or read instructions in ways designers did not intend. As systems get more capable, the gap between what people meant and what the model does can grow. AI already affects hiring, medical diagnoses, financial trading, and content ranking.

Incomplete data or hidden biases can reinforce discrimination, spread false claims, or cause economic harm.

Alignment work builds methods to catch failures early, add fairness constraints, and add fail-safes. Governments and companies are drafting standards that products must meet before they ship. Researchers treat model behavior like a contract and build checks that the system cannot break agreed rules even in new situations.

As AI drives cars, manages energy grids, and negotiates contracts, alignment is what decides whether those systems help or create new harm.

Specification gaming is the usual failure: the system hits the metric and misses the intent. Hiring, diagnosis, trading, and ranking already show how incomplete data and bias create harm, including discrimination, false content, and money loss. Alignment research builds evaluations, constraints, and tripwires.

Draft standards from governments and firms try to keep products behind those checks before customers see them. Contract-style verification asks whether a system can violate a stated rule in a novel case. Cars, grids, and contract agents raise the cost of getting this wrong. Preference learning is the practical alignment method labs ship today.

The deeper "whose values" problem is not solved by RLHF.