The explanation game: a formal framework for interpretable machine learning.

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation(s) for a given algorithmic prediction. Throug...

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Bibliographic Details

Published in:	Synthese Vol. 198; no. 10; pp. 9211 - 9243
Main Authors:	Watson, David S., Floridi, Luciano
Format:	Article
Published:	Springer Nature Oct2021
Subjects:	Machine learning Decision theory Statistical learning Polynomial time algorithms Explanation
Online Access:	View this record in EBSCOhost