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.

Inner alignment asks the question - “Is the model trying to do what humans want it to do?”, or in other words can we robustly aim our AI optimizers at any objective function at all?