The present perspective outlines how epistemically baseless and ethically pernicious paradigms are recycled back into the scientific literature via machine learning (ML) and explores connections between these two dimensions of failure. We hold up the renewed emergence of physiognomic methods, facilitated by ML, as a case study in the harmful repercussions of ML-laundered junk science. A summary and analysis of several such studies is delivered, with attention to the means by which unsound research lends itself to social harms. We explore some of the many factors contributing to poor practice in applied ML. In conclusion, we offer resources for research best practices to developers and practitioners.

From The reanimation of pseudoscience in machine learning and its ethical repercussions here: https://www.cell.com/patterns/fulltext/S2666-3899(24)00160-0. It's open access.

In other words ML--which includes generative AI--is smuggling long-disgraced pseudoscientific ideas back into "respectable" science, and rejuvenating the harms such ideas cause.

#AI #GenAI #GenerativeAI #LLMs #MachineLearning #ML #AIEthics #science #pseudoscience #JunkScience #eugenics #physiognomy