A Nonperson Predicate is a theorized test used to distinguish between a person and anything that isn't a person. The need for such a test arises from the possibility that when an Artificial General Intelligence predicts a person's actions, it may develop a model of them so complete that the model itself qualifies as a person. As the AGI investigates possibilities, all the negative situations the model experiences would generate a large amount of negative utility. Simulating a sufficiently complex model of a person is a computational hazard. Such a situation may be avoidable by limiting the complexity of any model of a person that an AGI creates, as discussed in Computational Hazards.
Any practical implementation would likely consist of a large number of nonperson predicates of increasing complexity. For most nonpersons, an predicate will quickly return that it is not a person and conclude the test. Although any number of the predicates may be used before the test claims that something is not a person, it is crucial that any predicate in the test never claims that a person isn't. If unavoidable, it is preferable that the AGI considers nonpersons persons than considering a person a nonperson.