AI testing is not that feasible as classical software testing. In a classical software, we know whats happening at every point of time. When you have a problem, you exactly know where it occurs and how to rectify them. Hence we would use white-box testing which touches the internals of the software. But in a AI software, testing the software internals is of no use. After a model is trained, we have no clue whats happening inside all those networks, it just happens. Hence the best option is to follow the black-box testing approach in which we test the functionality of the software from the outside.
AI testing is not that feasible as classical software testing. In a classical software, we know whats happening at every point of time. When you have a problem, you exactly know where it occurs and how to rectify them. Hence we would use white-box testing which touches the internals of the software. But in a AI software, testing the software internals is of no use. After a model is trained, we have no clue whats happening inside all those networks, it just happens. Hence the best option is to follow the black-box testing approach in which we test the functionality of the software from the outside.