  Artificial neural networks (ANNs) perform extraordinarily on numerous tasks
including classification or prediction, e.g., speech processing and image
classification. These new functions are based on a computational model that is
enabled to select freely all necessary internal model parameters as long as it
eventually delivers the functionality it is supposed to exhibit. Here, we
review the connection between the model parameter selection in machine learning
(ML) algorithms running on ANNs and the epistemological theory of neopragmatism
focusing on the theory's utility and anti-representationalist aspects. To
understand the consequences of the model parameter selection of an ANN, we
suggest using neopragmatist theories whose implications are well studied.
Incidentally, neopragmatism's notion of optimization is also based on utility
considerations. This means that applying this approach elegantly reveals the
inherent connections between optimization in ML, using a numerical method
during the learning phase, and optimization in the ethical theory of
consequentialism, where it occurs as a maxim of action. We suggest that these
connections originate from the way relevance is calculated in ML systems. This
could ultimately reveal a tendency for specific actions in ML systems.
