应用一个社区参与的参与式机器学习模型
Emmanuella Ngozi Asabor1,2, Kammarauche Aneni3,4, Sitara Weerakoon5,6
1Yale School of Medicine, New Haven, Connecticut, USA.
American journal of community psychology
|September 16, 2024
概括
在医疗保健领域的机器学习可以使不平等持续下去. 社区参与的参与模式,优先考虑社区洞察力,可以通过将算法植根于生活经验来缓解偏见并改善健康结果.
科学领域:
- 公共卫生 公共卫生
- 社区心理学 社区心理学
- 医疗信息学 医疗信息学
背景情况:
- 预测算法被提出为解决医疗保健偏差的解决方案.
- 机器学习 (ML) 技术有可能延续现有的健康不平等.
- 社区背景对于理解和减轻ML偏见至关重要.
研究的目的:
- 为健康领域的ML研究提出一个社区参与的参与模式.
- 使用社区见解概述了在ML算法中减轻偏差的原则.
- 在医疗保健中促进公平有效的ML应用.
主要方法:
- 开发一个社区参与的ML研究参与模式.
- 在所有研究阶段整合社区洞察力:优先设定,问题制定,假设定义和解释.
- 建立指导原则:共享决策,反射性和结构性谦卑,灵活性和适应性.
主要成果:
- 社区洞察力将代表性不足的群体定位为他们生活经验的专家.
- 在生活经验中将ML接地,确保算法在道德上是合理和有效的.
- 算法科学家和社区之间的双向伙伴关系至关重要.
结论:
- 社区参与的参与模式对于在健康领域的无偏见的ML至关重要.
- 纳入社区利益相关者确保算法是有效的和有道德依据的.
- 这种方法赋予社区权力,并减轻健康不平等.
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