Machine Learning Models and Their Development Process as Learning Affordances for Humans

This paper explores the relationship between unsupervised machine learning models, and mental models of those who develop or use them. In particular, we consider as well ‘organisational co-learning process’ that creates them, affordances. The process involves inputs originating both from human participants’ shared semantics, data. By combining these, resulting computational afford a newly shaped model, which is potentially more resistant to biases models. We illustrate this organisational with case study involving modelling via commonly used methods such dimension reduction clustering. Our describes how trading training company engaged in process, its behavior were (and afforded) by model. argues kind can play significant role learning, shaping safeguarding precisely because are unsupervised, thus lead unexpected inexplicit patterns.