The ethics mirror? Comparing LLM and human responses to ethical dilemmas of varying complexity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it