TY - GEN
T1 - AI in Smart Manufacturing
T2 - 4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024
AU - Onu, Peter
AU - Pradhan, Anup
AU - Madonsela, Nelson Sizwe
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The rapid advancement and deployment of Artificial Intelligence (AI) technologies in the manufacturing sector and the integration of the Internet of Things (IoT) and big data analytics are transforming traditional manufacturing processes. This is significant for Smart Manufacturing and poses immense potential for creating highly efficient, flexible, and interconnected production systems. However, these technological advancements come with substantial ethical and legal considerations that necessitate careful examination and proactive management. Using a qualitative research approach, this paper analyzes the ethical implications of AI implementation in manufacturing, exploring key challenges and opportunities for promoting responsible AI development. The paper presents a comprehensive review of emerging engineering solutions to address ethical concerns and protect intellectual property in the context of AI-driven Smart Manufacturing. It discusses the potential of blockchain technology in ensuring data integrity and transparency, federated learning approaches for collaborative model training while preserving data privacy, and AI transparency mechanisms to enhance trust and accountability in AI systems. Based on previous cases, industry methods, and scholarly discussions, this study provides practical suggestions for dealing with the ethical and intellectual property challenges of integrating AI into manufacturing processes.
AB - The rapid advancement and deployment of Artificial Intelligence (AI) technologies in the manufacturing sector and the integration of the Internet of Things (IoT) and big data analytics are transforming traditional manufacturing processes. This is significant for Smart Manufacturing and poses immense potential for creating highly efficient, flexible, and interconnected production systems. However, these technological advancements come with substantial ethical and legal considerations that necessitate careful examination and proactive management. Using a qualitative research approach, this paper analyzes the ethical implications of AI implementation in manufacturing, exploring key challenges and opportunities for promoting responsible AI development. The paper presents a comprehensive review of emerging engineering solutions to address ethical concerns and protect intellectual property in the context of AI-driven Smart Manufacturing. It discusses the potential of blockchain technology in ensuring data integrity and transparency, federated learning approaches for collaborative model training while preserving data privacy, and AI transparency mechanisms to enhance trust and accountability in AI systems. Based on previous cases, industry methods, and scholarly discussions, this study provides practical suggestions for dealing with the ethical and intellectual property challenges of integrating AI into manufacturing processes.
KW - artificial intelligence
KW - blockchain technology
KW - ethical AI
KW - federated learning
KW - intellectual property rights
KW - legal considerations
KW - smart manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85218346489&partnerID=8YFLogxK
U2 - 10.1109/IMITEC60221.2024.10850928
DO - 10.1109/IMITEC60221.2024.10850928
M3 - Conference contribution
AN - SCOPUS:85218346489
T3 - Proceedings of 2024 4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024
SP - 290
EP - 295
BT - Proceedings of 2024 4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024
A2 - Zuva, Tranos
A2 - Brown, Andrew
A2 - Rikhotso, Musa
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 November 2024 through 29 November 2024
ER -