What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems
The paper frames the relationship between medicine and machine learning as a generative analogy, using tools from Mary Hesse's philosophy of science to analyze how clinical translation standards can inform ML development It identifies and clarifies the epistemic and methodological warrants of clinical translation that are often invoked but rarely specified in the ML literature The authors reinterpret clinical translation warrants through a reliabilist lens, proposing a novel form of ML reliabili
Analysis
TL;DR
- The paper frames the relationship between medicine and machine learning as a generative analogy, using tools from Mary Hesse's philosophy of science to analyze how clinical translation standards can inform ML development
- It identifies and clarifies the epistemic and methodological warrants of clinical translation that are often invoked but rarely specified in the ML literature
- The authors reinterpret clinical translation warrants through a reliabilist lens, proposing a novel form of ML reliabilism that is distinct yet compatible with existing accounts in the philosophy of AI
- The work addresses the growing uncertainty around ML deployment by drawing on medicine's established frameworks for handling epistemic risk and methodological rigor
- Published on arXiv in August 2026 by Emanuele Ratti and Lena Zuchowski, spanning machine learning, AI, and computers and society
Why It Matters
This paper offers a philosophical foundation for improving ML reliability by borrowing from medicine's well-developed standards for clinical translation, which could help practitioners and researchers establish clearer epistemic and methodological warrants for deploying ML systems in high-stakes domains. It bridges philosophy of science with practical AI concerns, providing a framework that could shape how the industry approaches trustworthiness and validation in ML systems.
Technical Details
- The paper employs Mary Hesse's framework of generative analogies to characterize the parallel between clinical translation processes and ML system development, moving beyond superficial comparisons to identify structural correspondences
- It systematically identifies epistemic warrants (justification of knowledge claims) and methodological warrants (procedural standards) in clinical translation that are typically only implicitly referenced when drawing the medicine-ML analogy
- The reliabilist interpretation reframes clinical warrants in terms of reliable belief-forming processes, adapting this philosophical approach to the context of ML system design and deployment
- The proposed ML reliabilism is positioned as distinct from but compatible with existing reliabilist accounts in the philosophy of AI, suggesting an evolution rather than a replacement of current theoretical frameworks
- The work sits at the intersection of machine learning (cs.LG), artificial intelligence (cs.AI), and computers and society (cs.CY), indicating its interdisciplinary scope
Industry Insight
- AI practitioners working in regulated or high-stakes domains (healthcare, finance, autonomous systems) should consider adopting structured analogy-based frameworks from mature fields like medicine to strengthen their validation and deployment pipelines
- The reliabilist approach to ML warrants could inform the development of more rigorous evaluation standards and certification processes as the industry faces increasing scrutiny over AI reliability and trustworthiness
- Researchers and engineers should engage more deeply with the philosophical foundations of their field, as interdisciplinary cross-pollination from established domains can yield novel frameworks for addressing persistent challenges in ML deployment and governance
Disclaimer: The above content is generated by AI and is for reference only.