个人或数据点? 伦理学,人工智能和参与性转向精神健康研究
Joshua August Skorburg1, Kieran O'Doherty2, Phoebe Friesen3
1Department of Philosophy, University of Guelph.
The American psychologist
|January 18, 2024
概括
心理健康研究面临的挑战是整合人工智能和机器学习 (AI / ML) 与参与方法. 人工智能/万能机器工具有可能使参与者变得客观化,这与研究中的参与性转变相冲突.
科学领域:
- 心理健康研究 心理健康研究
- 计算精神病学是一种计算精神病学.
- 医疗保健中的人工智能
背景情况:
- 人工智能和机器学习 (AI / ML) 在心理健康研究中越来越热情.
- 参与式方法在科学研究中越来越受到重视,特别是在心理健康领域.
- 人工智能/ML方法通常依赖于广泛的传感器数据,可能对研究参与者进行客观化.
研究的目的:
- 在心理健康研究中识别和检查AI/ML采用和参与方法之间的紧张关系.
- 突出AI/ML在疏远参与者的风险,并将他们视为数据点.
- 探索如何将参与性转变的必要性与AI/ML方法相协调.
主要方法:
- 在心理健康研究中对AI/ML方法的概念分析.
- 检查AI/ML固有的假设和数据收集方法.
- 批评AI/ML与参与式研究原则之间的潜在冲突.
主要成果:
- 人工智能/机器学习的日益使用与参与式研究的原则之间存在着显著的紧张关系.
- 人工智能/机器学习的以数据为中心的方法,由多式联络传感器数据推动,有可能将参与者减少到仅仅是数据点.
- 在心理健康研究中,参与性转变的核心原则可能与当前的AI/ML实践有着根本的矛盾.
结论:
- 在心理健康研究中整合AI/ML需要仔细考虑,以避免破坏参与者的参与.
- 解决AI/ML假设和参与方法之间的冲突对于道德和有效的研究至关重要.
- 需要制定战略,以确保AI/ML工具增强,而不是削弱以参与者为中心的心理健康研究.
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