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

中文翻译：

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了解客观性在机器学习和研究评估中的作用

本文为提高机器学习（ML）研究的客观性提供了理由。任何声称拥有利益的研究工作都必须根据许多参数进行审查，例如所采用的方法，道德考量及其理论或技术贡献。我们从博物学家的哲学观点着手进行这一讨论。尽管每种分析都可能是主观的，但对于研究界而言，重要的是要不断审查研究以实现持续增长并产生更好的工作。我们建议以客观的方式标准化机器学习研究中的某些步骤，并意识到威胁客观性的各种偏见。客观性的理想使研究保持理性，因为客观性要求信念要基于事实。我们讨论了当前的一些挑战，