Evaluating the Social Impact of AI in Manufacturing: A Methodological Framework for Ethical Production

Hengyi Zang; Shaojie Li; Xinqi Dong; Danqing Ma; Bo Dang

doi:10.5281/zenodo.10474511

Authors

Hengyi Zang Independent Research
Shaojie Li Independent Research
Xinqi Dong Independent Research
Danqing Ma Independent Research
Bo Dang Independent Research

DOI:

https://doi.org/10.5281/zenodo.10474511

References:

8

Keywords:

AI in Manufacturing, Social Implications, Ethical Production

Abstract

At a time when artificial intelligence (AI) is transforming the manufacturing landscape, it is important to understand its impact on society. This paper presents a comprehensive framework for evaluating and ensuring the ethical production of AI in an integrated manufacturing environment.By taking an in-depth look at the impact of AI on the workforce, economic dynamics, and ethical issues, this study highlights the need for a balanced approach that both drives technological progress and embraces social responsibility. Using case studies and participatory methods, this study aims to explore the practical application of ethical codes in different manufacturing environments. The findings suggest a range of policy recommendations and regulatory strategies to drive responsible AI integration in manufacturing.

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Author Biographies

Hengyi Zang, Independent Research

Independent Research, Shanghai, China. E-mail: zanghengyizhy@gmail.com

Shaojie Li, Independent Research

Independent Research, Beijing, China. E-mail: 1454342484@qq.com

Xinqi Dong, Independent Research

Independent Research, Beijing, China. E-mail: dongxinqi@hotmail.com

Danqing Ma, Independent Research

Independent Research, Beijing, China. E-mail: 3530761316qq@gmail.com

Bo Dang, Independent Research

Independent Research, Fremont CA, US. E-mail: dangdaxia@gmail.com

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Zong, Yanqi, et al. “Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 29-35, doi:10.53469/jtpes.2023.03(12).05.