Topic Modeling of the AI Ethics, Generative AI, and Large Language Model Literature with Latent Dirichlet Allocation: Methodology and Preliminary Results
The rapid diffusion of generative artificial intelligence (AI) and large language models (LLMs) has produced a fast-growing, interdisciplinary body of scholarship on their ethical, social, regulatory, and safety implications. This paper presents the methodology and preliminary results of a Latent Dirichlet Allocation (LDA) topic model applied to a Scopus corpus of 20,000 records published between 2018 and 2026, of which 19,505 documents were retained after screening. Titles and abstracts were cleaned with a deterministic pipeline combining lemmatization, bigram detection, and four layers of stopword filtering. A 27-topic model with an asymmetric document-topic prior and $ta = 0.5$ ($C_v = 0.576$; NPMI = 0.082) was fitted with Gensim. The topics separate into a technical layer and a normative layer, with publication volume heavily concentrated in 2025–2026 and originating predominantly from the United States and China.