Social and environmental impact of recent developments in machine learning on biology and chemistry research
This work highlights critical but often overlooked negative impacts of ML in scientific domains, raising awareness for researchers in biology and chemistry.
The paper investigates the societal and environmental impacts of recent machine learning developments, such as increased resource use and biases, and shows through bibliometric analysis that these issues also affect applied ML in chemistry and biology, potentially influencing areas like drug discovery.
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.