Scientific Exploration and Explainable Artificial Intelligence

Minds and Machines 32 (1):219-239 (2022)
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Abstract

Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc analytic techniques from Explainable AI can be used to refine target phenomena in medical science, to identify starting points for future investigations of causal relationships, and to generate possible explanations of target phenomena in cognitive science. In this way, this paper describes how Explainable AI—over and above machine learning itself—contributes to the efficiency and scope of data-driven scientific research.

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Author Profiles

Carlos Zednik
Eindhoven University of Technology
Hannes Boelsen
Otto von Guericke Universität, Magdeburg

References found in this work

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - New York: Cambridge University Press.
Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
Four Decades of Scientific Explanation.Wesley C. Salmon & Anne Fagot-Largeault - 1989 - History and Philosophy of the Life Sciences 16 (2):355.

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