Teaching Algorithmic Bias in a Credit-Bearing Course

Abstract

Information literacy instruction has become increasingly nuanced with the widespread adoption of critical approaches to teaching and the ACRL Framework. Librarians are already teaching information evaluation strategies, however, more work can be done in the area of understanding algorithmic decision making and bias. This column describes how a public university integrated lessons on algorithmic bias into a credit-bearing information literacy course for a general undergraduate audience. The activities and readings can be adapted to a one-shot instruction environment and the collaborative process for designing the curriculum is also shared.