The Moral Turing Test: Evaluating Human-LLM Alignment in Moral Decision-Making
<|reference_start|>The Moral Turing Test: Evaluating Human-LLM Alignment in Moral Decision-Making: As large language models (LLMs) become increasingly integrated into society, their alignment with human morals is crucial. To better understand this alignment, we created a large corpus of human- and LLM-generated responses to various moral scenarios. We found a misalignment between human and LLM moral assessments; although both LLMs and humans tended to reject morally complex utilitarian dilemmas, LLMs were more sensitive to personal framing. We then conducted a quantitative user study involving 230 participants (N=230), who evaluated these responses by determining whether they were AI-generated and assessed their agreement with the responses. Human evaluators preferred LLMs' assessments in moral scenarios, though a systematic anti-AI bias was observed: participants were less likely to agree with judgments they believed to be machine-generated. Statistical and NLP-based analyses revealed subtle linguistic differences in responses, influencing detection and agreement. Overall, our findings highlight the complexities of human-AI perception in morally charged decision-making.<|reference_end|>
@article{garcia2024the,
    title={The Moral Turing Test: Evaluating Human-LLM Alignment in Moral
  Decision-Making},
    author={Basile Garcia, Crystal Qian, Stefano Palminteri},
    journal={arXiv preprint arXiv:2410.07304},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07304},
    primaryClass={cs.HC cs.AI}
}