Considering the Ethics of Large Machine Learning Models in the Chemical Sciences

Foundation models, including large language models, vision-language models, and similar large-scale machine learning tools, are quickly becoming ubiquitous in society and in the professional world. Chemical practitioners are not immune to the appeal of foundation models, nor are they immune to the many risks and harms that these models introduce. In this work, I present the first analysis of foundation models using the lens of scientific ethics and chemical professional ethics. I find that common general-purpose foundation models are essentially incompatible with the moral practice of chemistry, though there are fewer ethical problems with chemistry-specific foundation models. My discussion, which includes environmental harm, epistemological risk, labor ethics, and more, concludes with an examination of how the harm associated with foundation models can be minimized and further poses a set of serious lingering questions for chemical practitioners and scientific ethicists.