Ethical dimensions of algorithmic literacy for college students: Case studies and cross-disciplinary connections

Abstract

This article addresses three key questions related to the ethical facets of algorithmic literacy. First, it synthesizes existing literature to identify six core ethical components, including bias, privacy, transparency, accountability, accuracy, and non-maleficence. Second, a crosswalk maps the intersections of these principles across the Association of College and Research Libraries' Framework for Information Literacy for Higher Education and the Association of Computing Machinery's Code of Ethics and Professional Conduct and Joint Statement on Principles for Responsible Algorithmic Systems. This analysis reveals significant overlap on issues like unfairness and transparency, helping prioritize topics for instruction. Finally, case studies showcase pedagogical strategies for teaching ethical considerations, informed by the crosswalk. Workshops for diverse undergraduates and computer science students employed reallife instances of algorithmic bias to prompt reflection on unintended harm, contestability, and responsible development. Pre-post surveys indicated expanded critical perspectives after the interventions. By systematically examining shared values and testing instructional approaches, this study provides practical tools to shape ethical thinking on algorithms. It also demonstrates promising practices for responsibly advancing algorithmic literacy across disciplines. Ultimately, fostering interdisciplinary awareness and multipronged educational initiatives can empower students to question algorithmic authority and biases.