No, with a but. We can have creative yet ethical problem-solving if the system has a complete system of ethics, but otherwise creativity will be unsafe by default.
One can classify AI decision-making approaches into two types: interpolative thinkers, and extrapolative thinkers.
Interpolative thinkers learn to classify and mimic whatever they're learning from, and don't try to give reasonable results outside of their training domain. You can think of them as interpolating between training examples, and benefitting from all of the mathematical guarantees and provisos as other statistical techniques.
Extrapolative thinkers learn to manipulate underlying principles, which allows them to combine those principles in previously unconsidered ways. The relevant field for intuition here is numerical optimization, of which the simplest and most famous example is linear programming, rather than the statistical fields that birthed machine learning. You can think of them as extrapolating beyond training examples (indeed, many of them don't even require training examples, or use those examples to infer underlying principles).
The promise of extrapolative thinkers is that they can come up with these 'lateral' solutions much more quickly than people would be able to. The problem with these extrapolative thinkers is that they only use the spoken principles, not any unspoken ones that might seem too obvious to mention.
An attribute of solutions to optimization problems is that the feature vector is often 'extreme' in some way. In linear programming, at least one vertex of the feasible solution space will be optimal, and so simple solution methods find an optimal vertex (which is almost infeasible by nature of being a vertex).
As another example, the minimum-fuel solution for moving a spacecraft from one position to another is called 'bang-bang,' where you accelerate the craft as quickly as possible at the beginning and end of the trajectory, coasting at maximum speed in between.
While a virtue when the system is correctly understood (bang-bang is optimal for many cases), this is catastrophic when the system is incorrectly understood. My favorite example here is Dantzig's diet problem (discussion starts on page 5 of the pdf), where he tries to optimize his diet using math. Under his first constraint set, he's supposed to drink 500 gallons of vinegar a day. Under his second, 200 bouillon cubes. Under his third, two pounds of bran. The considerations that make those obviously bad ideas aren't baked into the system, and so the system innocently suggests them.
If you can completely encode the knowledge and values that a person uses to judge these plans into the AI, then extrapolative systems are as safe as that person. They'll be able to consider and reject the wrong sort of extreme plans, and leave you with the right sort of extreme plans.
But if you can't, then it does make sense to not build an extrapolative decision-maker, and instead build an interpolative one. That is, instead of asking itself "how do I best accomplish goal X?" it's asking itself "what would a person do in this situation?". The latter might be much worse at accomplishing goal X, but it has much less of the tail risk of sacrificing other goals to accomplish X.