论文标题
机器学习对生物学和化学研究的最新发展的社会和环境影响
Social and environmental impact of recent developments in machine learning on biology and chemistry research
论文作者
论文摘要
潜在的社会和环境影响，例如迅速增加的资源使用以及相关的环境影响，可重复性问题和排他性，ML研究的私有化，导致公共研究的脑力研究，对深度学习的关注引起的研究工作的狭窄以及由于缺乏社会化的偏见而引起的偏见是由于缺乏社会学的流动性而引起的，该研究是由数据和人员进行的，而在计算机上进行了一个计算，而这是一项计算机的开发。但是，这些讨论和出版物主要集中在计算机科学的贴发领域，包括计算机视觉和自然语言处理或基本ML研究。使用对开放式文献的完整和全文分析的文献计量分析，我们表明可以在化学和生物学中对应用机器学习进行相同的观察结果。这些发展可能会影响基础研究和应用研究，例如药物发现和开发，超出已知的有偏见数据集的问题。
Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to a public research brain-drain, a narrowing of the research effort caused by a focus on deep learning, and the introduction of biases through a lack of sociodemographic diversity in data and personnel caused by recent developments in machine learning are a current topic of discussion and scientific publications. However, these discussions and publications focus mainly on computer science-adjacent fields, including computer vision and natural language processing or basic ML research. Using bibliometric analysis of the complete and full-text analysis of the open-access literature, we show that the same observations can be made for applied machine learning in chemistry and biology. These developments can potentially affect basic and applied research, such as drug discovery and development, beyond the known issue of biased data sets.
