Supervised or unsupervised learning? Investigating the role of pattern recognition assumptions in the success of binary predictive prescriptions

Machine learning (ML) employs classification algorithms such as artificial neural networks to make automated decisions. While the proposed solutions have made significant contributions toward improving decision-making efficiency, their effectiveness to serve our multifaceted and complex society has recently come under question. This paper attempts to theorize an answer to why ML lacks effectiveness by studying the assumptions that are made to facilitate efficient pattern recognition. Specifically, this study recognizes five assumptions and investigates their influence on the effectiveness of decision-making for three well-known case studies. The results suggest including the assumptions needed for metric-optimizing supervised learning can only be justifiable and lead to decision-making effectiveness for cases in which a fair and equitable definition of success can be formulated as an objective function. In contrast, the results show using unsupervised learning or non-metric-optimizing supervised learning leads to a more reasonable balance of effectiveness and efficiency when the formulation of a fair and equitable definition of success is not possible. Moreover, the results demonstrate that the current ML approaches that employ supervised learning can improve their efficacy by rethinking the assumptions made at the stage of pattern recognition.