@inproceedings{li-etal-2025-human,
    title = "Human-{AI} Moral Judgment Congruence on Real-World Scenarios: A Cross-Lingual Analysis",
    author = "Li, Nan  and
      Kang, Bo  and
      De Bie, Tijl",
    editor = "Zhang, Chen  and
      Allaway, Emily  and
      Shen, Hua  and
      Miculicich, Lesly  and
      Li, Yinqiao  and
      M'hamdi, Meryem  and
      Limkonchotiwat, Peerat  and
      Bai, Richard He  and
      T.y.s.s., Santosh  and
      Han, Sophia Simeng  and
      Thapa, Surendrabikram  and
      Rim, Wiem Ben",
    booktitle = "Proceedings of the 9th Widening NLP Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.winlp-main.10/",
    pages = "46--49",
    ISBN = "979-8-89176-351-7",
    abstract = "As Large Language Models (LLMs) are deployed in every aspect of our lives, understanding how they reason about moral issues becomes critical for AI safety. We investigate this using a dataset we curated from Reddit{'}s r/AmItheAsshole, comprising real-world moral dilemmas with crowd-sourced verdicts. Through experiments on five state-of-the-art LLMs across 847 posts, we find a significant and systematic divergence where LLMs are more lenient than humans. Moreover, we find that translating the posts into another language changes LLMs' verdicts, indicating their judgments lack cross-lingual stability."
}