在健康数据供应链管理的背景下,道德采购:一种价值敏感的设计方法
Camille Nebeker1, Jean Christophe Bélisle-Pipon2, Benjamin X Collins3,4,5
1Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, United States.
JAMIA open
|October 23, 2025
概括
Bridge2AI开发了使用价值敏感设计和供应链管理为伦理来源的健康数据存储库的实用指南. 这确保了AI/ML研究的数据完整性和信任,减轻了诸如伦理洗等风险.
科学领域:
- 生物医学和行为研究研究.
- 机器学习 (ML) / 人工智能 (AI)
- 数据科学与管理数据科学与管理
背景情况:
- 桥2AI计划旨在为健康数据存储库建立道德标准.
- 伦理来源的数据对于有效的ML/AI研究至关重要,但定义最初未定义.
- 需要一个实际的,操作的框架来指导存储库的创建和道德数据采购.
研究的目的:
- 为伦理来源的健康数据库制定定义和指导方针.
- 通过使用价值敏感设计 (VSD) 方法,探索健康数据库开发中的伦理紧张关系.
- 为了使数据完整性,偏差缓解和对AI/ML系统的信任的伦理价值观变得可操作.
主要方法:
- 采用价值敏感设计 (VSD) 方法,与供应链管理 (SCM) 流程集成.
- 确定了关键参与者,相关价值 (可追溯性,问责制,安全性) 和价值权衡.
- 开发了一个SCM框架来管理复杂的数据流,并嵌入偏差缓解策略.
主要成果:
- 识别了在存储库开发中影响伦理数据采购的行为体,价值观和紧张关系.
- 在模型前阶段,SCM步骤为道德采购提供了支架,涵盖来源,隐私,公平和公共利益.
- 突出了诸如"伦理洗"等挑战,以及需要透明,以价值为导向的做法.
结论:
- 将VSD与SCM集成,使伦理价值观变得可操作,增强数据完整性和对AI/ML的信任.
- 存储库开发中的基本决策显著影响数据质量和AI/ML系统的可用性.
- 优先考虑透明度,问责制和SCM等运营框架对于有影响力的健康数据库至关重要.
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