Ethical Risk and Pathway of AIGC Cross-Modal Content Generation Technology
DOI:
https://doi.org/10.58885/ijllis.v9i1.85.ljKeywords:
Human‒Machine Communication, AIGC Cross-Modal Content Generation Technology, Technology Accompaniment, Human‒Machine Community with a Shared Future.Abstract
This study analyses the core technologies underlying AI-generated cross-modal content (AIGC), identifying data, algorithms, and computing power as the fundamental pillars supporting AIGC operation. And data are recognized as the underlying logic driving AI's continuous development and the source of ethical issues within AIGC. By integrating Gilbert Hottois' concept of technological accompaniment, this research incorporates multiple stakeholders to dissolve the binary opposition between humans and machines. This study explores pathways to scientifically and positively advance AIGC technologies at the micro, medium, and macro levels. It advocates for human‒machine symbiosis, enhances the frequency and potential of users' digital interactions, improves their understanding and autonomy in applications, and promotes digital literacy in the intelligent era. Additionally, it emphasizes the importance of government-led initiatives and global dialog to establish a multistakeholder regulatory framework and conventions, aiming to create a more harmonious human‒machine community with a shared future.
References
Zhan, X., Li, B., & Sun, J. (2023). Scenario-based application and development opportunities of AIGC in the context of digital intelligence integration. Journal of Library and Information Knowledge 01: 75-85+55. DOI: 10.13366/j.dik.2023.01.075.
China Academy of Information and Communications Technology (2022) Artificial Intelligence White Paper.
Available at: http://www.caict.ac.cn/kxyj/qwfb/bps/202204/P020220412613255124271.pdf (accessed 10 July 2023).
Liu, H., Chen, J., Li, L., Bao, B., Li, Z., Liu, J., & Nie, L. (2023). Cross-modal representation and generation technology. Chinese Journal of Image and Graphics 06: 1608-1629. DOI: 10.11834/jig.230035.
Peng, L. (2023a). AIGC and the new survival characteristics of the intelligent era. Nanjing Social Sciences 05: 104-111. DOI: 10.15937/j.cnki.issn1001-8263.2023.05.011.
Peng, L. (2024) Human actors in intelligent communication. Journal of Northwest Normal University (Social Science Edition) 61(04): 25-35. DOI: 10.16783/j.cnki.nwnus.2024.04.003.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014), Generative adversarial nets. Advances in Neural Information Processing Systems 27: 2672-2680. DOI: 10.48550/arXiv.1406.2661.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, AN., ... and Polosukhin, I. (2017) Attention is all you need. Advances in Neural Information Processing Systems 30. DOI: 10.48550/arXiv.1706.03762.
Ho, J., Jain, A., and Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33: 6840-6851. DOI: 10.48550/arXiv.2006.11239.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... and Sutskever, I. (2021, July). Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, 8748-8763. PMLR.
DOI: 10.48550/arXiv.2103.00020.
Chen, Y. (2023). AIGC infringement risks and digital copyright protection strategies in the context of the intelligent era. Communication and Copyright 17: 113-116. DOI: 10.16852/j.cnki.45-1390/g2.2023.17.030.
Thomas, K., and Zheng, Y. (2007). Digital Anthropology. Central Compilation and Translation Press.
Zhang, X. (2023). Algorithmic governance challenges and governance-based supervision of generative artificial intelligence. Modern Jurisprudence 03: 108-123.
Pi, J. (2010). Social amplification of risk. China Labor and Social Security Press.
Jiang, H. (2023). Coexistence and symbiosis between humans and ChatGPT: From the “digital divide” to the “digital intelligence divide” — Taking Japan’s “skill reshaping” plan as an example. Journal of Yuejiang 03: 74-83+174. DOI: 10.13878/j.cnki.yjxk.20230426.007.
Verbeek, P.P., and Yang, Q. (2013). Accompanying technology: Philosophy of technology after the ethical turn. Journal of Luoyang Normal University 04: 18-21. DOI: 10.16594/j.cnki.41-1302/g4.2013.04.001.
Yu, Guoming., Lin, Yutong., Li, Yunyue. (2024). Generative AI as a new content productivity: Development limitations and future directions. Publishing Horizon 14: 22-30. DOI: 10.16491/j.cnki.cn45-1216/g2.2024.14.004.
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