Deep Learning and Neural Networks: Decision-Making Implications

Abstract

Deep learning techniques have found applications across diverse fields, enhancing the efficiency and effectiveness of decision-making processes. The integration of these techniques underscores the significance of interdisciplinary research. In particular, decisions often rely on the output’s projected value or probability from neural networks, considering different values of the relevant output factor. This interdisciplinary review examines the impact of deep learning on decision-making systems, analyzing 25 relevant papers published between 2017 and 2022. The review highlights improved accuracy but emphasizes the need for addressing issues like interpretability, generalizability, and integration to build reliable decision support systems. Future research directions include transparency, explainability, and real-world validation, underscoring the importance of interdisciplinary collaboration for successful implementation.