Ethical Challenges and Considerations for Responsible AI in Engineering: Examining the Ethical Reasoning of Large Language Models

Abstract

The widespread adoption of AI in engineering applications has created an urgent need to examine its ethical implications, as these technologies increasingly influence societal outcomes and create complex moral challenges. This study aimed to investigate two critical aspects: the evolution of AI applications across engineering disciplines and the ethical reasoning capabilities of large language models (LLMs). By examining patent publications, we traced how AI implementation in industrial engineering has evolved, revealing a clear progression from basic simulation-based approaches to sophisticated optimization techniques, and ultimately to contemporary machine-learning methodologies. To complement this technical analysis, we assessed six LLMs using established ethical sensitivity measurements previously validated with engineering students. Our findings revealed that while these models demonstrated proficiency in identifying ethical issues within presented scenarios, they exhibited notable inconsistencies when confronted with practical ethical dilemmas. These results make a significant contribution to our understanding of AI’s expanding role in engineering and its capacity for ethical decision-making, while simultaneously highlighting the pressing need to develop more robust ethical frameworks as these technologies become further entrenched in engineering practice. This research underscores the importance of maintaining stringent ethical standards in AI development and implementation, suggesting that continued refinement of ethical guidelines should parallel technological advancement in the engineering sector.

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