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Large-scale moral machine experiment on large language models
http://hdl.handle.net/10228/0002001684
http://hdl.handle.net/10228/0002001684834ca099-3abd-4fff-b5a0-8fa4937bdbf6
| 名前 / ファイル | ライセンス | アクション |
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10451321.pdf (7.7 MB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||
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| 公開日 | 2025-05-23 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Large-scale moral machine experiment on large language models | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Ahmad, Muhammad Shahrul Zaim bin
× Ahmad, Muhammad Shahrul Zaim bin
× 竹本, 和広
WEKO
24877
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| 著作権関連情報 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 権利情報 | Copyright (c) 2025 Ahmad, Takemoto. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |||||||||||
| 言語 | en | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | The rapid advancement of Large Language Models (LLMs) and their potential integration into autonomous driving systems necessitates understanding their moral decision-making capabilities. While our previous study examined four prominent LLMs using the Moral Machine experimental framework, the dynamic landscape of LLM development demands a more comprehensive analysis. Here, we evaluate moral judgments across 52 different LLMs, including multiple versions of proprietary models (GPT, Claude, Gemini) and open-source alternatives (Llama, Gemma), to assess their alignment with human moral preferences in autonomous driving scenarios. Using a conjoint analysis framework, we evaluated how closely LLM responses aligned with human preferences in ethical dilemmas and examined the effects of model size, updates, and architecture. Results showed that proprietary models and open-source models exceeding 10 billion parameters demonstrated relatively close alignment with human judgments, with a significant negative correlation between model size and distance from human judgments in open-source models. However, model updates did not consistently improve alignment with human preferences, and many LLMs showed excessive emphasis on specific ethical principles. These findings suggest that while increasing model size may naturally lead to more human-like moral judgments, practical implementation in autonomous driving systems requires careful consideration of the trade-off between judgment quality and computational efficiency. Our comprehensive analysis provides crucial insights for the ethical design of autonomous systems and highlights the importance of considering cultural contexts in AI moral decision-making. | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : PLoS ONE 巻 20, 号 5, p. e0322776-1-e0322776-20, 発行日 2025-05-21 |
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| 出版社 | ||||||||||||
| 出版者 | Public Library of Science | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1371/journal.pone.0322776 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 1932-6203 | |||||||||||
| 査読の有無 | ||||||||||||
| 値 | yes | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000509_ja.html | |||||||||||
| 論文ID（連携） | ||||||||||||
| 値 | 10451321 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 14491 | |||||||||||
