人工智能系统在生物医学中的因果关系和科学解释
Florian Boge1, Axel Mosig2,3
1Institute for Philosophy and Political Science, Technical University Dortmund, Emil-Figge-Str. 50, 44227, Dortmund, Germany.
Pflugers Archiv : European journal of physiology
|October 29, 2024
概括
生物医学中的可解释的人工智能 (XAI) 需要科学解释来建立可信度. 这种方法可以确保人工智能决策是可靠的,对患者的福祉是公平的.
科学领域:
- 生物医学人工智能
- 机器学习 机器学习
- 哲学 哲学 是一个哲学.
背景情况:
- 人工智能 (AI) 系统在生物医学中越来越多地使用,实现了高精度,但引发了可信度问题.
- 可解释的人工智能 (XAI) 旨在使AI决策易于理解,但解释和可信度之间的联系尚不清楚.
研究的目的:
- 提出生物医学AI中的解释必须符合科学解释标准,以促进可信度.
- 探索AI中的解释,可信度,因果关系和稳定性之间的关系.
主要方法:
- 这是一个跨学科的方法,结合了生物医学,机器学习和哲学.
- 对近期基于人工智能的病理学研究进行审查.
- 在AI中讨论因果关系和随机干预.
主要成果:
- 科学解释对于建立生物医学AI可信度至关重要.
- 将AI解释连接到因果关系和稳定性是可靠AI应用的关键.
结论:
- 生物医学AI可信度依赖于科学解释,而不仅仅是预测准确性.
- 提供了指导方针,以整合人工智能在生物医学与科学解释原则.
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