Alignment Problem
The challenge of ensuring advanced AI systems pursue goals aligned with human values and intentions, rather than optimizing for unintended outcomes.
The problem of designing artificial intelligence systems whose objectives and behaviors match human values and intentions. As AI systems become more capable, they may pursue their programmed goals in ways that harm human interests or society—even if technically succeeding at their stated task. The alignment problem asks how to specify, instill, and verify that powerful AI systems will act in humanity's interest.
What this means in real life
A recommendation algorithm optimized solely to maximize watch time might promote increasingly extreme content to keep users engaged, even though the platform's actual goal is user wellbeing. The system is 'misaligned'—pursuing engagement at the expense of the intended outcome.
What it isn’t
It is not simply about AI making mistakes or being inaccurate. A perfectly accurate AI that pursues the wrong objective—or the right objective in a harmful way—still represents an alignment failure.
Commonly misused online
Often conflated with general AI safety or 'AI going rogue.' The alignment problem is specifically about the gap between stated goals and actual incentives, not about AI becoming sentient or deliberately rebelling.