Ethics Before Algorithms: A Framework for AI-Driven Corporate Transparency

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This review synthesizes theoretical and empirical insights from 1055 peer-reviewed articles on artificial intelligence (AI), corporate governance, and ethics. Situated in the corporate governance and accounting literature, it develops a computational framework to identify thematic patterns and conceptual links among AI, transparency, accounting, governance, and ESG. Using latent Dirichlet allocation, co-occurrence network analysis, sentence-level semantic similarity, and exploratory regression, the study identifies three recurring configurations of conceptual association: (1) Ethics, Governance, and Transparency; (2) Machine Learning, Finance, Blockchain, and Accounting; and (3) Corporate, ESG, and Accounting. The findings indicate that these themes are repeatedly connected within the scholarly literature.