A prior is whatever you start with. There’s literally no requirements. Bayes tells you how to update your priors, whatever they are, in the face of new data. Nothing more, nothing less. In principle it doesn’t matter what priors you start with (how biased they are), in the sense that given enough data, your likelihoods will converge to what is really the case.

If you have the right model.

You can set your priors equally. 100% / number of possible outcomes.