在多语言抑郁体中探索与使用大型语言模型相关的偏见
Paula Andrea Perez-Toro1,2,3, Judith Dineley4, Raquel Iniesta4
1Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. paula.andrea.perez@fau.de.
Scientific reports
|October 16, 2025
概括
大型语言模型 (LLM) 显示了检测抑郁症的潜力,但人口偏差会影响表现. 年龄和性别显著影响跨语言的LLM准确性,突出需要公平的心理健康AI.
科学领域:
- 人工智能在心理健康中的作用
- 计算语言学 计算语言学
- 临床心理学 临床心理学
背景情况:
- 大型语言模型 (LLM) 为检测和监测主要抑郁症 (MDD) 提供了新的途径.
- 现有的LLM可能会表现出人口偏见,可能会影响其在不同人群中的表现.
- 确保人工智能工具在精神卫生保健中的公平性能至关重要.
研究的目的:
- 调查人口因素 (年龄,性别) 对LLM绩效在分类抑郁症症状严重性的影响.
- 评估跨多语言数据集 (英语,西班牙语,荷兰语) 的LLM绩效.
- 识别和解决与人口和语言多样性相关的LLM准确性的潜在差异.
主要方法:
- 对多语言数据集进行系统的平衡和评估,以对抑郁症症状严重程度进行分类.
- 基于年龄和性别代表性的LLM绩效差异的分析.
- 对LLMs的跨语言绩效评估.
主要成果:
- 年龄在各种模型和语言中对LLM绩效产生了一致和明显的影响.
- 性别对模型性能产生了不同的影响.
- 在英语,西班牙语和荷兰语数据集中观察到模型准确度的明显差异.
结论:
- 人口因素,特别是年龄,在心理健康应用中显著影响LLM的表现.
- 语言多样性和人口代表性是公平的AI在医疗保健中的关键考虑因素.
- 需要进一步的研究来开发具有人口意识的LLM,并减轻偏见,以改善心理健康查的概括性.
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