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<p>Automated discovery techniques, driven by computational methods, have played an important role in achieving scientific breakthroughs across various disciplines. This paper investigates the impact of automated discovery on scientific understanding through the examination of two case studies: quantum optics experiment design and accelerated materials discovery of vanadium selenites. I argue that these systems enrich researchers’ understanding by revealing novel phenomena and uncovering solutions that lie beyond the confines of manual search, yet nonetheless once discovered can be integrated into pre-existing theoretical scaffolding. I further study the role played by machine learning models involved in automated discovery as it relates to the increase scientific understanding, arguing that they can be taken to be INUS conditions. Recognizing the capacity of automated discovery to improve understanding, the paper submits that the logic of discovery remains crucially relevant and warrants further in-depth conceptual development.</p>

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Is Automated Discovery Expanding Human Understanding? On the Role of Machine Learning in the Increase of Scientific Understanding

  • 期刊论文

  • 发表日期：

    2025年8月7日

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  • Kristian G. Barman

摘要

Automated discovery techniques, driven by computational methods, have played an important role in achieving scientific breakthroughs across various disciplines. This paper investigates the impact of automated discovery on scientific understanding through the examination of two case studies: quantum optics experiment design and accelerated materials discovery of vanadium selenites. I argue that these systems enrich researchers’ understanding by revealing novel phenomena and uncovering solutions that lie beyond the confines of manual search, yet nonetheless once discovered can be integrated into pre-existing theoretical scaffolding. I further study the role played by machine learning models involved in automated discovery as it relates to the increase scientific understanding, arguing that they can be taken to be INUS conditions. Recognizing the capacity of automated discovery to improve understanding, the paper submits that the logic of discovery remains crucially relevant and warrants further in-depth conceptual development.