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Philosophy-informed Machine Learning

delete2025-12-12
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M
M.Z. Naser
DOI：10.1016/j.asoc.2025.114427delete
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Abstract

Abstract

En 中文
• Philosophy-informed machine learning (PhIML) directly infuses philosophy into ML model architectures. • PhIML promises new capabilities through models that respect philosophical concepts and values by design. • We demonstrate philosophical gains and alignment. • We present case studies on how ML users/designers can adopt PhIML as an agnostic post-hoc tool or intrinsically build it into ML model architectures.
Keywords:
Engineering
Artificial intelligence
Philosophy
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Applied Soft Computing cover
Applied Soft Computing
IF:
6.6
Papers:
1.4W
Citations:
4.8W

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No organization information available