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机器学习中伪科学的复苏及其伦理影响

Mel Andrews^1,2, Andrew Smart³, Abeba Birhane^4,5

¹The University of Cincinnati, Cincinnati, OH, USA.

Patterns (New York, N.Y.)

|November 21, 2024

概括

机器学习 (ML) 可以无意中恢复有缺陷的科学思想,如生理学,导致有害的垃圾科学. 这种观点考察了糟糕的ML实践是如何造成社会伤害的,并为更好的研究提供了解决方案.

科学领域:

科学完整性科学完整性
机器学习伦理学机器学习伦理学
研究方法研究方法论.

背景情况:

在科学文献中重新引入了在认识论上毫无根据和在伦理上有害的范式.
机器学习 (ML) 促进了错误的科学概念的回收.
由于ML的进步,伪科学方法的生理学方法正在重新浮现.

研究的目的:

分析机器学习如何洗净垃圾科学.
探索ML失败与伦理/认识论问题之间的联系.
用面貌的复苏作为 ML 驱动的科学伤害的案例研究.

主要方法:

分析现有文献和ML实践的视角文章.
用ML促进的生理学研究的案例研究分析.
检查导致应用不良的ML的因素.

主要成果:

ML可以无意中使不健全的科学研究合法化和传播.
将ML应用于有缺陷的范式导致了"ML洗的垃圾科学".
应用ML的不良做法有助于有害研究的传播.

结论:

解决应用机器学习中的不良做法对于科学完整性至关重要.
在机器学习研究中开发和实施最佳实践可以减轻垃圾科学的传播.
为开发人员和从业人员提供资源,以提高研究质量和道德考虑.

关键词:

人工智能伦理学深度学习是一种深度学习.认识论的认识论学.机器学习是机器学习.科学哲学的科学哲学.物理形态学物理形态学伪科学是一种伪科学.

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