AI alignment is the field concerned with making AI systems do what their designers and users actually intend — pursuing the right goals, respecting human values, and avoiding harmful or unintended behavior, especially as systems grow more capable.
A foundational statement of the technical problem is the 2016 paper “Concrete Problems in AI Safety” by Amodei, Olah, Steinhardt, Christiano, Schulman, and Mane. It catalogs practical failure modes — such as systems gaming their objectives or causing side effects — and argues these deserve concrete engineering attention rather than only philosophical debate. OpenAI’s 2022 InstructGPT paper (Ouyang et al.) shows alignment in practice: it notes that bigger models are not automatically better at “following a user’s intent” and uses human feedback to close that gap.
Alignment spans both near-term concerns (toxic or biased output) and longer-term ones (controlling highly capable systems).
Why business readers should care: Alignment determines whether an AI behaves safely and predictably in your product. Gaps show up as reputational, legal, and safety risk, which is why major labs invest heavily here and why it shapes regulation.