From the Black Box to Understanding: Strengthening the Model-Phenomenon Link through Explainable Artificial Intelligence
DOI:
https://doi.org/10.48160/18532330me16.442Keywords:
scientific understanding, link uncertainty, black box models, explainable artificial intelligenceAbstract
This article addresses the challenge of deriving scientific understanding from machine learning models. Building on Sullivan (2022), I argue that opaque models can provide understanding if the link to the phenomenon they represent is strengthened. To this end, I propose integrating explainable artificial intelligence methods to identify relevant features in the input and, based on these, collect empirical evidence guided by theoretical knowledge to reduce uncertainty.
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