Teaching Machine Learning in the Context of Critical Quantitative Information Literacy

Carrie Diaz Eaton

Keywords: Machine Learning, ICML, OER, algorithmic bias, quantitative literacy

TL;DR: Teaching machine learning ideas in a prerequisite-free quantitative literacy class with careful critical inquiry.

Abstract: Bates College, is a small liberal arts postsecondary institution in the northeast United States. An information literacy course, Calling Bull, serves as an introductory data science class as well as a prerequisite-free quantitative literacy class. In this context, we spend a week discussing machine learning, with an emphasis on facial recognition algorithms. The emphasis is on the general algorithmic approach, critical inquiry of the process and careful interpretation of results presented in research or decision-making. This module relies on the use of open educational materials, discussion, and careful attention to issues of marginalization and algorithmic justice.