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.

人工神经网络在分类，预测等任务上表现出色，本研究回顾了在人工神经网络上运行的机器学习算法中模型参数选择与新实用主义理论之间的联系，探讨了优化在机器学习系统中的作用以及伦理学理论中的最佳行动的优化的内在关联。