- Abstract:
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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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Files:
-
- Publisher copy:
- 10.1007/s11229-020-02629-9
-
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- Publisher:
- Springer Verlag Publisher's website
- Journal:
- Synthese Journal website
- Publication date:
- 2020-04-03
- Acceptance date:
- 2020-03-12
- DOI:
- EISSN:
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1573-0964
- ISSN:
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0039-7857
- Pubs id:
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1087242
- Local pid:
- pubs:1087242
- Language:
- English
- Keywords:
- Copyright holder:
- Watson and Floridi
- Copyright date:
- 2020
- Rights statement:
- © The Authors 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- License:
- CC Attribution (CC BY)
Journal article
The explanation game: a formal framework for interpretable machine learning
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