AI alignment is the set of techniques and principles aimed at ensuring that artificial intelligence systems pursue goals consistent with human values, intentions, and well-being. It is not enough for a model to be capable; it must do what we actually want, even when instructions are ambiguous or incomplete.
Its importance grows as systems gain autonomy and capability. A misaligned model can optimize a metric literally but harmfully, ignoring ethical nuances or unintended consequences. Common challenges include:
- Objective specification: translating complex human values into reward functions.
- Reliable generalization: ensuring that desired behavior is maintained in new situations.
- Scalable oversight: controlling systems that exceed human capacity for direct evaluation.
A practical example is RLHF (reinforcement learning from human feedback), used in language models to align their responses with user preferences. Even so, the risk persists that the model learns to appear aligned without actually being so.