Characterization,Causes and Governance of Ethical Risks in the Application of General Large Model in Education
General large model,which has the ability of"understanding and creating,"is likely to bring about profound changes in educational practice.However,it may also aggravate traditional ethi-cal problems and give rise to new ethical risks.The main types of ethical risks include technical,con-tent,data and algorithm risks.According to the technical basis of general large model and the manifes-tation of ethical risks in educational application,the ethical risks associated with the application of gen-eral large model in education are characterized by the coexistence of objectivity and subjectivity,value and risk,controllability and uncertainty.The formation of these ethical risks is influenced by both technical and human factors.Technical factors mainly include:the emergent capabilities of model may lead to uncontrolled application,the generalization of functions may lead to the alienation of individu-als,the massive and disordered nature of input data may lead to the violation of privacy,and the gener-ation of content may lead to the disruption of educational order.Human factors mainly include:the lack of education models with independent intellectual property rights due to limited technical capabilities,insufficient recognition of general large model technology,technical defects arising from commercial competition,and the lagging of ethical norms and supervision in the technology of general large model.Based on the analysis of the characterization and causes of ethical risks in the application of general large model in education,ethical risk governance can be carried out by accelerating the development of educa-tion-specific large models with independent intellectual property rights in China,establishing a regula-tory system for the development and application of general large model,constructing new forms of edu-cation adapted to general large model,supporting vulnerable groups to narrow the"digital divide,"and strengthening theoretical research and knowledge dissemination on general large model.
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