偏见的人工智能:医疗保健AI中积极偏见的例子
Hurmat Ali Shah1, Zain Ul Abideen Tariq1, Marco Agus1
1College of Science and Engineering, Hamad bin Khalifa University, Qatar.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究重新定义了人工智能 (AI) 偏见作为公平的工具,提出了一个框架来纠正医疗保健差异,并通过使用目标驱动的AI改善边缘化群体的结果.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 人工智能伦理学 人工智能伦理学
- 卫生公平性健康公平性
背景情况:
- 人工智能 (AI) 在医疗保健中的偏见往往使系统性不平等延续.
- 现有的算法可能低估了黑人患者的风险,或者无法诊断黑人个体.
- 这些偏见强化了医疗保健和结果的差异.
研究的目的:
- 提出一个创新的框架来重新利用人工智能偏见作为解决结构性不公正的工具.
- 利用人工智能偏见改善代表性不足和边缘化群体的健康结果.
- 将人工智能偏见重新定义为提高医疗保健公平性的机制.
主要方法:
- 在AI系统中进行彻底的偏见分析.
- 策划用于人工智能培训的多样化和代表性数据集.
- 微调人工智能模型以与特定的公平目标和公平目标保持一致.
主要成果:
- 展示目标驱动偏见的潜力,以纠正系统性医疗保健差异.
- 展示人工智能如何被故意利用来提高诊断和医疗干预中的公平性.
- 突出"有偏见的人工智能"的能力,以推动更具包容性的医疗保健实践.
结论:
- 人工智能偏见,当故意重新使用时,可以成为实现健康公平的强大工具.
- 拟议的框架提供了一种新的方法来缓解现有的偏见并促进公平.
- 这一战略可以带来更公平的医疗保健系统,并改善所有群体的患者结果.
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