Outer alignment asks the question—“What should we aim our model at?” In other words, is the model optimizing for the correct reward such that there are no exploitable loopholes? It is also known as the reward misspecification problem.

Overall, outer alignment as a problem is intuitive enough to understand, i.e., is the specified loss function aligned with the intended goal of its designers? However, implementing this in practice is extremely difficult. Conveying the full “intention” behind a human request is equivalent to conveying the sum of all human values and ethics. This is difficult in part because human intentions are themselves not well understood. Additionally, since most models are designed as goal optimizers, they are all susceptible to Goodhart’s Law which means that we might be unable to foresee negative consequences that arise due to excessive optimization pressure on a goal that would look otherwise well specified to humans.

To solve the outer alignment problem, some sub-problems that we would have to make progress on include specification gaming, value learning, and reward shaping/​modeling. Some proposed solutions to outer alignment include scalable oversight techniques such as IDA, as well as adversarial oversight techniques such as debate.

Outer Alignment vs. Inner Alignment

This is often taken to be separate from the inner alignment problem, which asks: How can we robustly aim our AI optimizers at any objective function at all?

It should be kept in mind that you can have both inner and outer alignment failures together. It is not a dichotomy and often even experienced alignment researchers are unable to tell them apart. This indicates that the classifications of failures according to these terms are fuzzy. Ideally, we don’t think of a binary dichotomy of inner and outer alignment that can be tackled individually but of a more holistic alignment picture that includes the interplay between both inner and outer alignment approaches.