Exploring AI Ethics Syllabi Through NLP Cluster Analysis - Kerrie Hooper; Stephanie Lunn (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
With new technology comes new responsibilities. Examining AI through an ethical lens has become increasingly important and significant. Numerous professional organizations, such as IEEE have developed AI ethics guidelines. Additionally, academia is essential for fostering innovation and developing gifted people who work in both the ethical and technical spheres. Academia prepares students to be culturally responsive, have a collaborative mindset with interdisciplinary skills, and be aware of equity issues and other social problems that plague society. To assess the content in academia, a Natural Language Processing (NLP) analysis of AI ethics syllabi at the university level was conducted. A total of 45 AI ethics syllabi that are publicly available and online were used in this study. Some important features captured from each syllabus are the course description, topics, department, and year. Using various NLP tools for analysis, a general exploration of AI ethics curricula was conducted. Through supervised and unsupervised clustering and Latent Dirichlet Allocation (LDA) analysis, various patterns in the contents of the AI ethics syllabus were found. Some of these include patterns from syllabi across various academic departments and the pre-post Chat-GPT era. This study is insightful as it acts as a baseline for investigating various AI ethics topics that converge across academic departments, as well as uncovering potential gaps in AI ethics syllabus contents.