大型语言模型中的道德刻板印象
Aliah Zewail1, Alexandra Figueroa2, Jesse Graham3
1Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA 01003.
概括
大型语言模型 (LLM) 难以准确估计全球道德价值观,经常刻板印象非西方人口. 这些模型对跨文化道德估计存在重大伦理和认识风险.
科学领域:
- 计算社会科学 计算社会科学
- 跨文化心理学 跨文化心理学
- 人工智能伦理学 人工智能伦理学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于数据分析,但它们在代表各种人类价值观方面的准确性尚不清楚.
- 了解跨文化道德价值观对于全球合作和道德AI发展至关重要.
研究的目的:
- 评估LLMs在估计48个国家的人口道德价值观的准确性.
- 将LLM产生的道德观念与六个关键道德价值观的大规模调查数据进行比较.
主要方法:
- 在48个国家,LLM被要求估计"平均"人的道德规范.
- 研究结果与实证调查数据对六个道德价值观进行了比较:关心,平等,比例,忠诚,权威和纯洁.
主要成果:
- 在捕捉全球道德多样性方面,LLM的准确性很差,系统地歪曲道德价值观.
- 模型高估了诸如关怀之类的价值,低估了诸如纯洁之类的价值.
- 法律学士表现出偏见,高估了西方的道德问题,低估了非西方的道德问题,表明了刻板印象.
结论:
- 在产生跨文化道德价值估计时,LLM是不可靠的.
- 由于固有的偏见和不准确性,对此类任务的依赖LLM带来了重大的伦理和认识风险.
- 这些发现强调了在敏感的跨文化研究中使用LLM时需要谨慎.
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