应该要求所有护士完成隐含偏见培训吗?
Tanya Bartley1, Rebecca O'Connor, Kenya Beard
1Tanya Bartley is director of clinical simulation at the Mercy University School of Nursing, Dobbs Ferry, NY. Rebecca O'Connor is an associate professor at the University of Washington School of Nursing in Seattle. Kenya Beard is dean and chief nursing officer at the Mercy University School of Nursing, and a member of AJN's editorial board. Contact author: Tanya Bartley, tbartley5@mercy.edu. The authors have disclosed no potential conflicts of interest, financial or otherwise.
系统性的偏见缓解对于公平的结果至关重要. 一种结构化的方法可以确保在研究和实践中获得可靠和公平的结果.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 数据和算法的偏差可能导致不公平或歧视性的结果.
- 识别和解决偏见对于道德AI和研究至关重要.
- 现有的缓解偏差的方法往往缺乏统一的,系统的框架.
研究的目的:
- 提出一种系统的方法来缓解偏见.
- 为识别,分析和减少偏见提供一个结构化的框架.
- 提高人工智能系统和研究方法的公平性和公平性.
主要方法:
- 对当前偏差缓解技术的文献综述.
- 为系统偏见缓解制定一个概念框架.
- 案例研究分析,以证明框架的应用.
主要成果:
- 拟议的框架概述了关键阶段:偏见检测,表征,缓解和验证.
- 框架的系统应用可以带来明显更公平的结果.
- 这种方法可以适应各种领域和各种类型的偏见.
结论:
- 系统的方法对于有效的偏差缓解是不可或缺的.
- 实施结构化框架可以提高人工智能和研究的可靠性和公平性.
- 进一步的研究应该集中在完善和验证这个系统的方法在各种应用程序.
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