Conference item icon

Conference item

Explaining explanations in AI

Abstract:

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that "All models are wrong but some are useful." We foc...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1145/3287560.3287574

Authors


More by this author
Institution:
University of Oxford
Division:
SSD
Department:
Oxford Internet Institute
Role:
Author
ORCID:
0000-0002-4709-6404
More by this author
Institution:
University of Oxford
Division:
Social Sciences Division
Department:
Oxford Internet Institute
Role:
Author
More from this funder
Name:
British Academy
Grant:
PF170151
More from this funder
Name:
Alan Turing Institute
Grant:
EPSRC grant EP/N510129/1
Publisher:
Association for Computing Machinery
Host title:
FAT* '19 Proceedings of the Conference on Fairness, Accountability, and Transparency
Journal:
ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*) More from this journal
Pages:
279-288
Publication date:
2019-01-29
Acceptance date:
2018-10-13
DOI:
ISBN:
9781450361255
Keywords:
Pubs id:
pubs:937081
UUID:
uuid:f6049f9a-bfae-4694-800a-7b07a5e92a67
Local pid:
pubs:937081
Source identifiers:
937081
Deposit date:
2018-11-04

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP