Work

The reanimation of pseudoscience in machine learning and its ethical repercussions

Year: 2024

Type: review

Abstract: 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.

Source: Patterns

DOI: 10.1016/j.patter.2024.101027

Authors: Mel Andrews, Andrew Smart, Abeba Birhane

Institutions: University of Cincinnati, Carnegie Mellon University, Google (United States), Mozilla Foundation, Trinity College Dublin

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,900

Funder: Colorado Mesa University