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Philosophy-informed Machine Learning
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DOI：10.1016/j.asoc.2025.114427.png)
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Abstract
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• 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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