Understanding the Role of Objectivity in Machine Learning and Research Evaluation

This article makes the case for more objectivity in Machine Learning (ML) research. Any research work that claims to hold benefits has be scrutinized based on many parameters, such as methodology employed, ethical considerations and its theoretical or technical contribution. We approach this discussion from a Naturalist philosophical outlook. Although every analysis may subjective, it is important community keep vetting continuous growth produce even better work. suggest standardizing some of steps ML an objective way being aware various biases threatening objectivity. The ideal keeps rational since requires beliefs facts. discuss current challenges, role two elements (product process) are up consideration make recommendations support community.