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The explanation game: a formal framework for interpretable machine learning

Abstract:

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. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and rele...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
SSD
Department:
Oxford Internet Institute
Oxford college:
Green Templeton College
Role:
Author
ORCID:
0000-0001-9632-2159
More by this author
Division:
SSD
Subgroup:
Oxford Internet Institute
Role:
Author
ORCID:
0000-0002-5444-2280
Publisher:
Springer Verlag Publisher's website
Journal:
Synthese Journal website
Publication date:
2020-04-03
Acceptance date:
2020-03-12
DOI:
EISSN:
1573-0964
ISSN:
0039-7857
Pubs id:
1087242
Local pid:
pubs:1087242

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