Zednik, Carlos and Boelsen, Hannes (2020) The Exploratory Role of Explainable Artificial Intelligence. In: UNSPECIFIED.

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Abstract

Models developed using machine learning (ML) are increasingly prevalent in scientific research. Because many of these models are opaque, techniques from Explainable AI (XAI) have been developed to render them transparent. But XAI is more than just the solution to the problems that opacity poses—it also plays an invaluable exploratory role. In this paper, we demonstrate that current XAI techniques can be used to (1) better understand what an ML model is a model of, (2) engage in causal inference over high-dimensional nonlinear systems, and (3) generate algorithmic-level hypotheses in cognitive science.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Zednik, Carlos carlos.zednik@ovgu.de 0000-0002-9702-7706 Boelsen, Hannes hannes.boelsen@ovgu.de
Keywords:	Exploration, Machine Learning, Explainable AI, Opacity, Causal Inference, Algorithm
Subjects:	General Issues > Causation Specific Sciences > Cognitive Science > Computation General Issues > Computer Simulation Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User:	Dr. Carlos Zednik
Date Deposited:	18 Aug 2020 21:16
Last Modified:	18 Aug 2020 21:16
Item ID:	18005
Subjects:	General Issues > Causation Specific Sciences > Cognitive Science > Computation General Issues > Computer Simulation Specific Sciences > Artificial Intelligence > Machine Learning
Date:	2020
URI:	https://philsci-archive.pitt.edu/id/eprint/18005

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