  Deduction is the one of the major forms of inferences and commonly used in
formal logic. This kind of inference has the feature of monotonicity, which can
be problematic. There are different types of inferences that are not monotonic,
e.g. abductive inferences. The debate between advocates and critics of
abduction as a useful instrument can be reconstructed along the issue, how an
abductive inference warrants to pick out one hypothesis as the best one. But
how can the goodness of an inference be assessed? Material inferences express
good inferences based on the principle of material incompatibility. Material
inferences are based on modal vocabulary, which enriches the logical
expressivity of the inferential relations. This leads also to certain limits in
the application of labeling in machine learning. I propose a modal
interpretation of implications to express conceptual relations.
