Source: Jinwen&

Abstract: The research of He Miao and Chu Xingbo of Northwestern Polytechnical University focuses on the ethical risks of generative artificial intelligence, and proposes to build a logical system of ethical norms and risk avoidance strategies, which is of great value to promoting the healthy development of artificial intelligence and protecting human well-being. This article will interpret the research in detail in order to provide readers with a comprehensive and in-depth understanding.

Paper information: He Miao, Chu Xingbo. Ethical risks and normative selection of generative artificial intelligence [J]. Research on Dialectics of Nature, 2024, 40(11):94-101. DOI:10.19484/ki.1000-8934.2024.11.007.

introduction

With the rapid development of technology, generative artificial intelligence such as Sora has entered the public's vision. While its powerful functions have brought convenience to human life and work, it has also caused many ethical controversies. From privacy leakage, copyright infringement to information misleading, these issues not only affect individual rights and interests, but also pose challenges to social order, cultural inheritance, security and other aspects. Against this background, the research of He Miao and Chu Xingbo has important practical significance and theoretical value.

On the one hand, the study responds to the social concerns about the ethical issues of generative artificial intelligence. Today, with the increasing field of digital communication, artificial intelligence has been deeply integrated into human society, and its ethical risks are related to everyone's life. For example, privacy leakage may lead to personal property damage and reputation damage; the spread of false information may cause social panic and group confrontation. By deeply analyzing these risks, the research provides the public with a theoretical basis for understanding and responding to problems, which helps to enhance society's rational understanding of artificial intelligence.

On the other hand, this study enriches the theoretical system of artificial intelligence ethics. Previous research on artificial intelligence ethics has focused on the perspectives of human centers and artificial intelligence centers. Based on the practical theoretical basis of subject-mediation-object, the paper constructed a three-dimensional analysis framework of "human subject-technical mediation-virtual field", and proposed a set of systemic ethical risk governance solutions, which promoted theoretical innovation in artificial intelligence ethics.

The novelty of the paper

A unique research perspective. Unlike traditional single-view research, this paper places generative artificial intelligence in a three-dimensional framework of human subjects, artificial intelligence mediation, and virtual social fields for investigation. This comprehensive perspective helps to fully understand the ethical problems caused by generative artificial intelligence at different levels, providing a foundation for building systematic ethical norms.

The problem analysis is in-depth. The paper not only lists the ethical risks faced by generative artificial intelligence when integrating into the human order, participating in human-machine collaborative practice, and being in standardized service management, but also deeply analyzes the internal causes and harms of these risks. For example, the discussion on the problems of the generation of false information leading to the tearing of interpersonal relationships and the one-way question-and-answer dialogue leading to the dispersed beliefs of social groups reveals the profound impact of generative artificial intelligence on human society.

Solution innovation. In terms of strategy choices to avoid ethical risks, the paper proposes diversified strategies such as technological innovation and iteration, legal mechanism improvement, and moral education value shaping. These strategies cover multiple levels such as technology, law, and morality, reflect the idea of ​​systematic governance, and provide a new path to solving the ethical problems of generative artificial intelligence.

Research methods of papers

This paper mainly uses literature research method and theoretical analysis method to conduct in-depth discussions on the ethical risks and normative selection of generative artificial intelligence.

In terms of literature research, the author has extensively reviewed relevant research results at home and abroad, such as Ma Tiehong's legal prevention and control of risks in the application of artificial intelligence technology, and Li Zhixiang's moral hazard and ethical regulation of privacy digitalization. Through the review and analysis of these documents, the author has mastered the cutting-edge dynamics of generative artificial intelligence ethics research, providing a solid theoretical foundation for the research of papers.

In terms of theoretical analysis, the author uses multidisciplinary theories such as philosophy, ethics, and sociology to analyze the ethical risks of generative artificial intelligence. For example, learn from Heidegger's ontology to analyze the contradiction between the internal mechanism of generative artificial intelligence and human consciousness; use the Marxist theory of labor value to explore the impact of generative artificial intelligence on issues such as labor, property rights, etc. The comprehensive application of multidisciplinary theory has made the research strong theoretical depth and persuasiveness.

In addition, the paper also uses case analysis method to intuitively demonstrate the manifestation and harm of its ethical risks through specific case analysis of generative artificial intelligence such as Sora. For example, using face-changing fraud cases to illustrate the damage to interpersonal relationships by generating false information, and using AI singer cases to illustrate the risks of application of generative artificial intelligence in the cultural field.

The research process of the paper

The research process of the paper can be summarized into four stages: problem raising, risk analysis, normative construction and strategy selection.

At the stage of raising the problem, the author started from the current development status of generative artificial intelligence and the ethical controversy caused, and led to the research question: Does generative artificial intelligence need to establish ethical norms? What kind of ethical norms are needed? How to avoid its ethical risks? The proposal of these issues clarifies the research direction and lays the foundation for subsequent research.

In the risk analysis stage, the author analyzed the ethical challenges and difficulties it faces in detail from the three aspects of generative artificial intelligence integrating human existing order, participating in human-computer collaborative practice, and being in standardized service management. For example, in terms of integrating into the human order, challenges to industry transformation, patent confirmation, and subject selection are discussed; in terms of participating in human-machine collaborative practice, the problems of data sources, normative compliance and results deviations are analyzed. Through multi-dimensional risk analysis, the author comprehensively reveals the ethical dilemma of generative artificial intelligence.

In the standard construction stage, the author constructed a logical system of generative artificial intelligence ethical norms based on the three principles of people-oriented, algorithmic mediation, and virtual society. Among them, the principle of people-oriented emphasizes the status of human subjects and requires developers and users to pay attention to human ethics; the principle of algorithm mediation focuses on ethical issues in algorithm design, training and application processes to ensure the fairness, transparency and interpretability of algorithms; the principle of virtual society focuses on the characteristics of virtual society and formulates public cognitive ethical norms to prevent moral immorality and ethical confusion.

In the strategy selection stage, the author proposes specific strategies to avoid the ethical risks of generative artificial intelligence. In terms of technological innovation and iteration, it emphasizes optimizing the intelligent interactive experience of human-computer and improving the intelligence level of algorithms and models; in terms of improving legal mechanisms, it advocates deepening the regulatory supervision and evaluation system and formulating specific regulatory laws and regulations; in terms of shaping moral education value, it focuses on strengthening mainstream ideological guidance and improving public scientific and technological ethics. These strategies provide practical guidance for solving generative artificial intelligence ethics problems.

Conclusion and prospect of the paper

The main conclusion drawn by the paper is that while generative artificial intelligence brings convenience to human society, it also causes many ethical risks, and it is necessary to build a systematic ethical normative system and governance strategies to avoid these risks.

From the conclusion, this study provides theoretical support and practical guidance for the healthy development of generative artificial intelligence. By building an ethical normative system, the ethical principles that should be followed in the design, development and application of generative artificial intelligence are clarified; by proposing risk avoidance strategies, it provides a reference for multiple subjects such as governments, enterprises, developers, and users to participate in artificial intelligence governance.

Looking ahead, there is still room for further expansion and deepening of this research field. On the one hand, with the continuous development of generative artificial intelligence technology, new ethical issues may continue to emerge, and need continuous attention and research. For example, the application of artificial intelligence in the fields of medical care, education, justice, etc. may cause new ethical controversy, such as medical data privacy protection, educational equity, judicial fairness and other issues. On the other hand, the effectiveness of ethical norms and governance strategies needs to be continuously tested and improved in practice. Different countries and regions have differences in culture, law, social systems, etc. How to build a unified and effective generative artificial intelligence ethical norms and governance system on a global scale is a question worthy of in-depth discussion.

Conclusion

The research of He Miao and Chu Xingbo provides an analytical framework with both theoretical depth and practical value for the ethical governance of generative AI. Today, with the acceleration of technological iteration, the paper tells us that the construction of ethical norms should not be a passive adaptation to technology, but an active design that guides technology to goodness. In future research, we should continue to pay attention to new trends in the ethical issues of generative artificial intelligence, continuously expand and deepen the research content, and contribute to the construction of a harmonious human-machine symbiotic society.