Using KeyGraph and ChatGPT to Detect and Track Topics Related to AI Ethics in Media Outlets

Wei-Hsuan Li and Hsin-Chun Yu ()
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Wei-Hsuan Li: Department of Information Management, Tunghai University, Taichung 407224, Taiwan
Hsin-Chun Yu: Department of Information Management, Tunghai University, Taichung 407224, Taiwan

Mathematics, 2025, vol. 13, issue 17, 1-40

Abstract: This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, the research integrates the theory of chance discovery with the KeyGraph algorithm to conduct topic detection through a keyword network built through iterative semantic exploration. ChatGPT is employed for semantic interpretation, enhancing both the accuracy and comprehensiveness of the detected topics. Guided by the double helix model of human–AI interaction, the framework incorporates a dual-layer validation process that combines cross-model semantic similarity analysis with expert-informed quality checks. An analysis of 24 authoritative AI ethics reports published between 2022 and 2024 reveals a consistent trend toward semantic stability, with high cross-model similarity across years (2022: 0.808 ± 0.023; 2023: 0.812 ± 0.013; 2024: 0.828 ± 0.015). Statistical tests confirm significant differences between single-cluster and multi-cluster topic structures ( p < 0.05). The thematic findings indicate a shift in AI ethics discourse from a primary emphasis on technical risks to broader concerns involving institutional governance, societal trust, and the regulation of generative AI. Core keywords, such as bias, privacy, and ethics, recur across all years, reflecting the consolidation of an integrated governance framework that encompasses technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework contributes empirically to AI ethics governance and offers actionable insights for researchers and interdisciplinary stakeholders.

Keywords: AI ethics; topic detection; chance discovery; KeyGraph; keyword network; ChatGPT (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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