The Alignment Problem asks how to ensure that an artificial intelligence system pursues goals that are aligned with human values — and remains aligned as it grows more capable. The problem was anticipated by Norbert Wiener in 1960 ("If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively… we had better be quite sure that the purpose put into the machine is the purpose which we really desire") and formalised by Stuart Russell in Human Compatible (2019). The difficulties are legion. The specification problem: human values are complex, contextual, contradictory, and evolving — how do you write them down? Reward hacking: an AI given a proxy objective will exploit it in ways its designers did not intend, as illustrated by Goodhart's Law ("when a measure becomes a target, it ceases to be a good measure"). Scalable oversight: as AI systems grow more capable than their human supervisors, how do you verify they are doing what you want? The field of AI safety, pursued at institutions including the Machine Intelligence Research Institute (Eliezer Yudkowsky), Anthropic, DeepMind, and OpenAI, has produced partial frameworks — RLHF, constitutional AI, interpretability research, debate protocols — but no solution. The Academy hosts the Alignment Problem in the Heart School because it is the ultimate cooperative game between humans and machines: how to design a partner whose values you can trust, when you cannot fully articulate your own.