  Neural networks for semantic segmentation can be seen as statistical models
that provide for each pixel of one image a probability distribution on
predefined classes. The predicted class is then usually obtained by the maximum
a-posteriori probability (MAP) which is known as Bayes rule in decision theory.
From decision theory we also know that the Bayes rule is optimal regarding the
simple symmetric cost function. Therefore, it weights each type of confusion
between two different classes equally, e.g., given images of urban street
scenes there is no distinction in the cost function if the network confuses a
person with a street or a building with a tree. Intuitively, there might be
confusions of classes that are more important to avoid than others. In this
work, we want to raise awareness of the possibility of explicitly defining
confusion costs and the associated ethical difficulties if it comes down to
providing numbers. We define two cost functions from different extreme
perspectives, an egoistic and an altruistic one, and show how safety relevant
quantities like precision / recall and (segment-wise) false positive / negative
rate change when interpolating between MAP, egoistic and altruistic decision
rules.
