Algorithmic Black Box and Value Bias: Ethical Risk Scrutiny and Safety Governance of Generative AI Embedding in Content Production of Internet Ideological and Political Education in Universities

Abstract

With the rapid development of Generative Artificial Intelligence (AIGC) technologies and their deep embedding into the entire process of content production for Internet ideological and political education in universities, their powerful automated generation and multi-modal interaction capabilities have empowered educational innovation while simultaneously introducing deep ethical risks and security challenges. The inherent "algorithmic black box" attribute of Generative AI leads to the uninterpretability of content production and the concealment of responsible subjects. Meanwhile, potential biases in training data can easily trigger "value bias" in output content, manifesting as the implicit penetration of ideology, the breeding of historical nihilism, and the obscuration of mainstream values. Based on the perspective of the overall national security concept and Marxist technological ethics, this paper deeply scrutinizes the multi-dimensional ethical dilemmas caused by the embedding of Generative AI into university online ideological and political content production, such as data privacy leakage, the dissolution of teacher-student subjectivity, and the solidification of algorithmic discrimination. On this basis, the article systematically constructs a full-chain safety governance system covering "data source purification—algorithmic model alignment—human-machine collaborative review—dynamic risk monitoring" from four dimensions: technical governance, subject reshaping, institutional standardization, and literacy improvement. The aim is to build a strong line of defense for university online ideological security in the intelligent age and promote the organic unity of "tech for good" and educational goals.