AI Ethics as Epistemological Governance: A Systematic Literature Review on Knowledge and Authority in the Age of Generative AI - Cahyo Prianto; Mumu Komaro; Saripudin (Language, Technology, and Social Media, 2026)
This Systematic Literature Review (SLR) explores research trends on the ethics of generative AI use and how ethical issues are discussed within it, mapping geographical distribution and examining AI epistemic governance. This systematic literature review employs the PRISMA method, with a literature search conducted through the Scopus database, filtered based on keywords related to generative AI Ethics in Quartiles Q1 and Q2 and limited to the period 2020–2026, resulting in 39 articles for further analysis in this SLR. The research trend has continuously increased from year to year, and 2025 became the year with the highest number of studies addressing the ethics of generative AI use. This indicates a strengthening academic attention to ethical and epistemic issues in AI. The literature is dominated by themes of ethical concerns in the use of generative AI, such as bias, data privacy, transparency, accountability, misinformation, academic integrity, and cognitive dependence on generative AI. This study also finds that generative AI is most frequently positioned as a knowledge generator, while the combination of training data bias and cultural bias constitutes the most dominant epistemic issue. In the dimension of epistemic dependency, human dependence on AI is the most frequently discussed theme. This demonstrates growing concerns regarding the weakening of human autonomy, control, and cognitive capacity. From the perspective of authoritative actors, the scientific community occupies the strongest position, while multi-stakeholder governance emerges as the most widely supported governance model. These findings affirm that AI governance is understood as a complex issue that cannot be resolved by a single actor, but rather requires collaboration.