从人类的定量评分和定性叙述数据中比较情绪价值,使用人工智能来减少照顾者的差异
Sunmoo Yoon1, Robert Crupi2, Frederick Sun2
1General Medicine, Columbia University, New York, NY.
Studies in health technology and informatics
|April 9, 2025
概括
对非洲裔美国阿尔茨海默病患者护理人员的人工智能态度的机器学习分析揭示了负面的情感价值,与积极的人类评级形成鲜明对比. 这突显了人工智能驱动的情绪分析对不同人群的潜在偏见.
科学领域:
- 老年学是一门学科.
- 人工智能伦理学 人工智能伦理学
- 医疗信息学 医疗信息学
背景情况:
- 患有阿尔茨海默病和相关痴呆症 (ADRD) 的人的非裔美国家庭照顾者面临着独特的挑战.
- 了解护理人员对人工智能 (AI) 的态度对于开发支持性技术至关重要.
- 现有的调查方法可能无法完全捕捉护理人员情绪的细微差别.
研究的目的:
- 为了比较来自机器学习 (ML) 的自然语言处理 (NLP) 和人类评级的情感价值得分.
- 评估非洲裔美国家庭护理人员对人工智能在ADRD护理中的使用的看法.
- 评估NLP在定性调查数据的情绪分析中的可靠性.
主要方法:
- 在2024年4月至5月期间,对627名非裔美国人进行了一项调查,这些人是2024年4月至5月期间对ADRD患者的家庭照顾者.
- 参与者回答了关于10个AI用例的开放式问题,随后进行了评分.
- 应用了三个ML算法 (AFINN,Bing,Syuzhet) 来检测文本数据中的情感价值.
主要成果:
- 从NLP分析中获得的平均情绪价值得分在所有算法中始终为负 (AFINN: -1.61,Bing: -1.40,Syuzhet: -0.67).
- 人类对情绪价值的评分是积极的 (2.30).
- 在机器和人类评级之间发现了显著的差异 (p=0.0001).
结论:
- 使用NLP的基于机器的情感分析可能会误解或不准确地反映护理人员对AI态度的情感价值.
- 人类评级提供了对护理人员情绪的更积极和可能更准确的评估.
- 研究结果强调,在AI和NLP时代,需要仔细考虑调查设计和解释方法.
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