Research on the Construction of Digital Standard Contracts for Artificial Intelligence Data Training

Authors

  • Qiuhui Ren Henan Forestry Vocational College
  • Xiaohua Fu Chengdu Polytechnic

DOI:

https://doi.org/10.62177/apemr.v3i2.1246

Keywords:

Artificial Intelligence, Data Training Behavior, Digital Standard Contract

Abstract

Based on the analysis and comparative study of domestic and foreign literature, this paper focuses on the legal regulatory issues arising from the process of artificial intelligence (AI) data training. It aims to address the inherent contradictions between machine learning models and the technical characteristics of traditional legal norms, the lack of algorithmic transparency, and the identification of data ownership by constructing digital standard contracts. Corresponding development suggestions are put forward from three aspects: establishing a dynamic update mechanism for contracts, standardizing transnational data training cooperation, and deepening the legal effect of contract execution results.

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References

Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.; J. Yin, Trans.). Tsinghua University Press.

Guo, D., & Zhang, Y. (2024). Infringement risks of generative AI training data and legal responses. Journal of Xiangtan University (Philosophy and Social Sciences), 48(5), 78–86.

Jiang, H., & Tan, X. (2026). Beyond techno-nationalism: Governance dilemmas and regulatory approaches to international AI security. Contemporary China and World, (1), 65–75.

Wu, Y., Chen, Y., Yang, H., et al. (2024). Fragmentation of global digital governance under algorithmic institutional competition and its transcendence path. Modern Distance Education, 1–15.

Li, M. (2024). A report on the translation practice of international contract law: AI, data, cybersecurity and the legal landscape from the perspective of teleology [Master’s thesis]. Guangdong University of Foreign Studies.

Gao, X. (2025). Digital empowerment of dual contract risk control to improve the operational resilience of manufacturing enterprises. Information Construction, (6), 56–57.

He, C., & Ma, G. (2025). Analysis of computer communication technology and electronic information technology in the field of AI. China New Communications, 27(20), 16–18.

Liu, X. (2024). “Non-work use” in generative AI data training and its legitimacy justification. Legal Forum, 39(3), 67–78.

Zuo, S. (2026). Validity identification and dispute resolution path of electronic contracts in the context of digital economy. Legal Vision, (3), 52–54.

Crawford, K. (2021). The atlas of AI. Yale University Press.

Hu, X., & Zhou, Y. (2025). A review of research on economics and management disciplines based on generative AI. Chinese Journal of Management Science, 33(1), 76–97.

Cheng, X. (2026). On the inapplicability of network infringement rules to infringements of generative AI services. Comparative Law Review, (1), 125–137.

Cheng, Y., Chen, G., Chen, H., et al. (2022). Exploration on key technical paths of standard digitization based on AI. Information Technology and Standardization, (10), 60–67.

European Union. (2024). AI Act (EU AI Act).

China Academy of Information and Communications Technology. (2023). White paper on AI data training security.

Liu, S., Zhou, L., Yang, J., et al. (2022). Evolution of AI industry technology standard cooperation network and subject identification: Based on social network analysis and TOPSIS entropy weight method. Science and Technology Management Research, 42(6), 14–152.

Ma, S., Yi, Z., Pan, G., et al. (2026). Host country data privacy protection policies and China's digital service trade exports: Evidence from the EU General Data Protection Regulation. Finance and Economics, 42(1), 39–49.

European Commission. (2023). Guidelines on algorithm impact assessment.

Gao, Q. (2023). Legal realization path of algorithmic transparency. Journal of Political Science and Law, (4), 112–125.

Wu, Z. (2025). Justification of fair use path for AI data training: A comment on the first fair use cases in China and the United States. Journal of Shandong University of Science and Technology (Social Sciences), 27(6), 53–61.

Gao, Y. (2024). Regulation of copyright infringement by AI training data. China Publishing Journal, (15), 12–18.

Zhou, H. (2026). Judicial protection of AI models: From the perspective of unfair competition dispute case of “Transformation Comic Special Effect”. Journal of Law Application, (3), 71–86.

Shen, K. (2024). On the implementation mechanism of soft law: Taking AI ethical norms as an example. Finance and Economics Law Review, (6), 108–127.

IEEE. (2021). Standard for ethical aligned design (IEEE 7000-2021).

Liu, Y. (2026). How to achieve new breakthroughs in China's AI legislation? Practical solutions based on security, rights and governance. Law and Social Development, 32(2), 187–207.

Song, Y. (2025). Legal risks and regulatory paths of generative AI data cross-border flow. Cybersecurity and Data Governance, 1–8.

Cai, X. (2023). A comparative study on AI governance between China, the United States and Europe. Legal Forum, (3), 88–95.

Shen, F. (2025). Risks to judicial justice arising from the development of AI justice and its prevention and governance. Legal Research, 1–15.

Hu, B., Wu, J., & Zhang, S. (2024). Research on AI knowledge creation: Overall framework and future prospects. Journal of Hangzhou Dianzi University (Social Sciences), 20(5), 26–39.

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How to Cite

Ren, Q., & Fu, X. (2026). Research on the Construction of Digital Standard Contracts for Artificial Intelligence Data Training. Asia Pacific Economic and Management Review, 3(2). https://doi.org/10.62177/apemr.v3i2.1246

Issue

Section

Articles

DATE

Received: 2026-03-28
Accepted: 2026-03-30
Published: 2026-04-12