Earlier quoted context omitted.
Sure it is. NNs can behave non-deterministic (i.e. they produce different output for same input because AFAIK some operations on GPUs are undeterministic) and it makes sense to invest into software systems that clearly take a closer look on software correctness. One idea to get correcter code is clean code layout (I heard good things about the LLVM code base), simple to read code and compilers that exploit theoretica…
It ultimately depends on how much of the car's action is driven by DNNs, a miss-classification in the CNN can easily lead to a car crashing on a highway and causing a pileup. DNNs are so complex, you can't really write unit tests like you typically do for deterministic code. That's why some of the big banks flat out refuse to implement any form of deep learning for risk analytics. They're much more reliant on simpler…
In the end you want to know what did go wrong (the public will demand it and they are right) and it might be misclassification.
But that is not enough. You want to know why did it classify situation X wrong? So the answer is because the inference network (which was created via the training network) computed its weights because the input was Y. Now you might throw your hands in the air and say "oh it's complicated, the network is undeterministic, blame NVidia", but you can also go farther and build your networks deterministic (which is possible and AFAIK not a performance penalty). Compiler research helps in at least guaranteeing that certain parts of code are deterministic which makes it easier to debug and maybe avoid complex NN misclassification scenarios, but the way to do it doesn't have to do much with NN's itself but more with language design and (real-time, in particular deterministic) OS research.
So the statement oh, that's so complex, we do not no why we misclassified is no excusion, we can do better.
For starters we have to publish NN papers with implementations that describe how to make a particular NN out of given trainigs data (provide that too). We already publish the code and network structure (see caffe, etc.), but often with pre-trained models that have been build on a cluster with many forms of training-data going through network structure, etc.
Now at the moment you read a paper, head to the published code (often the case, again a desirable property of the ML community) and try to reproduce some examples by training on the data.
However it is hard to say in the end, if your network is really as good as the published, as a simple
$ diff my-net-binary-blob.dat tesla-net-binary-blob.dat