Nguyen, Timothy (2026) The Role of Rigor in Artificial Intelligence. [Preprint]
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Abstract
Artificial Intelligence (AI) has achieved extraordinary capabilities despite lacking many of the conceptual and scientific foundations associated with mature disciplines.
Unlike traditional sciences, where reliable technology typically emerges from theoretical understanding, modern AI has progressed largely through performance-driven iteration and ``alchemical'' experimentation.
This tension motivates a systematic analysis of AI through the lens of rigor.
We introduce a three-part framework consisting of conceptual rigor (clarifying foundational concepts), epistemic rigor (establishing scientific understanding), and operational rigor (ensuring reliable performance and deployment).
Using this framework, we analyze competing conceptions of intelligence and understanding, the strengths and limitations of the empirical approach to deep learning, the power and pitfalls of benchmarks, and the obstacles to theory development posed by modern AI systems.
We argue that the distinctive trajectory of AI arises from how forms of rigor interact across paradigms, resulting in the primacy of operational rigor in modern deep learning.
This perspective helps explain both AI’s rapid advances and its persistent uncertainties, while clarifying the challenges involved in transforming AI into a mature science and reliable technology.
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| Item Type: | Preprint | ||||||
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| Additional Information: | To appear in D. Rickles and T. Thebault (eds.) Philosophy of Rigour (Routledge, 2026) | ||||||
| Keywords: | artificial intelligence, rigor, machine learning | ||||||
| Subjects: | Specific Sciences > Artificial Intelligence Specific Sciences > Artificial Intelligence > Machine Learning |
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| Depositing User: | Dr Timothy Nguyen | ||||||
| Date Deposited: | 19 May 2026 12:32 | ||||||
| Last Modified: | 19 May 2026 12:32 | ||||||
| Item ID: | 29688 | ||||||
| Subjects: | Specific Sciences > Artificial Intelligence Specific Sciences > Artificial Intelligence > Machine Learning |
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| Date: | 18 May 2026 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/29688 |
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