The ethics mirror? Comparing LLM and human responses to ethical dilemmas of varying complexity

Abstract

The rise of large language models (LLMs) such as GPT has increased their use in business settings, yet uncertainty persists regarding their integration, particularly when facing ethical dilemmas traditionally managed by humans. To investigate how closely LLMs mimic human responses in real-world business ethical challenges, we conduct three experiments. We present ethical dilemmas of varying complexity and focus, and we assess the effect of a specific prompt – consequence enumeration – on eliciting ethical responses from GPT versus humans. Findings indicate that GPT alone is more ethical than humans in less complex dilemmas where unethical behavior admits a clear normative response, while both GPT and human responses are similarly (un)ethical in more complex dilemmas. The impact of consequence enumeration on curbing unethical responses varies between GPT and humans, depending on dilemma complexity and focus. These insights advance research on AI ethics and its applications in business, offering strategies to address ethical challenges and boost human agency in LLM-driven decision-making as AI becomes increasingly prevalent in business and society.

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