数据风险作为神经科学的伦理工具包
Susana Román García1, Ceilidh Welsh2, Nina H Di Cara3
1Centre for Discovery Brain Sciences, College of Medicine & Veterinary Medicine, Biomedical Sciences, University of Edinburgh, Edinburgh, UK.
Neuroethics
|February 21, 2025
概括
数据危险框架提供社区设计的标签,以识别和减轻数据科学项目的伦理风险. 这种方法应用于神经科学,促进计算建模的负责任创新.
科学领域:
- 神经科学是一个神经科学.
- 数据科学 伦理学 数据科学
背景情况:
- 数据科学中的伦理考虑对于负责任的研究至关重要.
- 数据危险框架为识别和减轻伦理风险提供了一个结构化的方法.
- 社区设计的标签有助于讨论和思考与数据相关的潜在危害.
研究的目的:
- 在神经科学领域引入和应用数据危险框架.
- 为了证明数据危险标签在神经科学研究中的实际实用性.
- 鼓励在计算神经科学项目中进行道德反思.
主要方法:
- 使用数据危险框架,该框架采用社区设计的警告标签.
- 将框架应用于一个特定的神经科学项目,重点是对突触后机制的计算建模.
- 说明危险标签如何突出伦理考虑和缓解策略.
主要成果:
- 数据危险框架可以有效地应用于神经科学研究.
- 危险标签为讨论计算神经科学中的伦理影响提供了一个切实的工具.
- 该框架有助于在数据驱动的神经科学中积极识别和管理伦理风险.
结论:
- 数据危害框架是加强神经科学中的伦理实践的宝贵工具.
- 采用数据危险标签可以促进对计算神经科学的更负责任和反思性的方法.
- 这种方法支持制定符合神经科学数据科学特定挑战的伦理准则.
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