This study analyzes the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing the scarcity of longitudinal perspectives in the literature. In response to the rapid AI technology evolution and associated ethical risks and societal influences, this research integrates the theory of chance discovery with the KeyGraph algorithm. Guided by the double helix model of human–AI interaction, this work constructs a keyword co-occurrence network via iterative semantic exploration. Building on the co-occurrence structures and original textual data, this work employs ChatGPT for semantic interpretation, enhancing the accuracy and comprehensiveness of topic detection. The study analyzes AI ethics reports released between 2022 and 2024 by reputable, authoritative institutions, revealing that the thematic focus has expanded from technical risks to broader issues of institutional governance and societal trust. Various keywords, including bias, privacy, and ethical, have emerged as core nodes across multiple years, indicating a shift in AI ethics discourse from technical development to regulatory policy. This evolution highlights the formation of an integrated governance framework, encompassing technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework offers empirical contributions to AI ethics governance and knowledge development and valuable insight for researchers and interdisciplinary stakeholders.
Li et al. (Thu,) studied this question.
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