Considering the ethics of large machine learning models in the chemical sciences

Abstract

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 combined lens of scientific ethics and chemical professional ethics. I find that general-purpose generative foundation models are in many ways incompatible with the moral practice of chemistry, though there are fewer ethical problems with chemistry-specific foundation models. My discussion 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.