跨学科的专业知识,以推进以人为中心的可解释AI
Chloe R Bennett1, Heather Cole-Lewis2, Stephanie Farquhar3
1Work done at Google via Pro Unlimited, Folsom, CA, USA; Northeastern University, Boston, MA, USA.
Social science & medicine (1982)
|February 7, 2026
概括
本研究介绍了一个以人为中心的人工智能 (AI) 模型,使用跨学科专家审查来识别和减少医疗保健中的偏见. 这种方法提高了AI的性能,并促进了未来研究的团队科学.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 偏差检测 偏差检测 偏差检测
背景情况:
- 人工智能 (AI) 显著影响医疗保健,但持续的偏见和子组绩效问题阻碍了它的全部潜力.
- 现有研究强调数据的代表性和模型性能,以减少偏见和促进人口健康.
- 社会流行病学,行为科学和卫生服务研究为解释AI发现提供了有价值的框架,特别是可解释AI (XAI).
研究的目的:
- 引入一种新型的人在循环模型,将生成XAI与跨学科专家小组相结合.
- 从多个角度批判性地评估AI模型解释,以确定偏见并指导未来的研究.
- 利用多学科的专业知识来提高AI的严谨性和促进团队科学.
主要方法:
- 将生成式可解释AI (XAI) 与一个全面的跨学科专家小组审查过程相结合.
- 以人为中心的方法批判性地评估AI模型解释.
- 通过专家解释将历史和上下文信息纳入.
主要成果:
- 跨学科专家小组有效地确定了人工智能模型中偏见的领域.
- 专家的解释提供了历史和上下文相关的见解.
- 确定了潜在的混因素,并整合了先前研究的科学证据,以减少偏见.
结论:
- 以人为中心的框架增强了人工智能模型改进和实施在医疗保健中的方法论严谨性.
- 这种方法积极促进团队科学和协作研究机会.
- 该模型提供了一条有希望的途径,可以减轻偏见,并改善卫生应用中的AI性能.
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