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<p>This paper examines the paradoxical decline in engagement with philosophy of science among engineers precisely when machine learning (ML) systems are increasingly performing complex epistemological functions in engineering practice. We identify how <i>philosophical naivety</i>, characterized by the uncritical adoption of reductive frameworks regarding consciousness, intelligence, and ethics, creates tangible organizational and technical liabilities. We then demonstrate how conceptual limitations in engineers’ philosophical foundations lead to three primary flaws: 1) ontological misclassification of system capabilities, 2) ethical blind spots in ML system design and application, and 3) inadequate epistemological approaches and hidden philosophical commitments for interpreting model outputs. Thus, we argue that renewed engagement with the philosophy of science is not merely academic but necessary for engineers to maintain epistemic authority and responsibility in an era where engineering judgment is increasingly delegated to or mediated by ML systems. In response, we propose a technical-philosophical framework integrating perspectives from philosophy of mind, ethics, epistemology, and engineering to address these shortcomings systematically.</p>

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On the Philosophical Naivety of Engineers in the Age of Machine Learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • M. Z. Naser

摘要

This paper examines the paradoxical decline in engagement with philosophy of science among engineers precisely when machine learning (ML) systems are increasingly performing complex epistemological functions in engineering practice. We identify how philosophical naivety, characterized by the uncritical adoption of reductive frameworks regarding consciousness, intelligence, and ethics, creates tangible organizational and technical liabilities. We then demonstrate how conceptual limitations in engineers’ philosophical foundations lead to three primary flaws: 1) ontological misclassification of system capabilities, 2) ethical blind spots in ML system design and application, and 3) inadequate epistemological approaches and hidden philosophical commitments for interpreting model outputs. Thus, we argue that renewed engagement with the philosophy of science is not merely academic but necessary for engineers to maintain epistemic authority and responsibility in an era where engineering judgment is increasingly delegated to or mediated by ML systems. In response, we propose a technical-philosophical framework integrating perspectives from philosophy of mind, ethics, epistemology, and engineering to address these shortcomings systematically.