Background: Science is experiencing a reproducibility crisis. Artificial intelligence research is not an exception. Objective: To give practical and pragmatic recommendations for how to document AI research so that the results are reproducible. Method: Our analysis of the literature shows that AI publications fall short of providing enough documentation to facilitate reproducibility. Our suggested best practices are based on a framework for reproducibility and recommendations given for other disciplines. Results: We have made an author checklist based on our investigation and provided examples for how every item in the checklist can be documented. Conclusion: We encourage reviewers to use the suggested best practices and author checklist when reviewing submissions for AAAI publications and future AAAI conferences.
%0 Journal Article
%1 GundersenEtAl2018Reproducible
%A Gundersen, Odd Erik
%A Gil, Yolanda
%A Aha, David W.
%D 2018
%J AI Magazine
%K Diss
%N 3
%P 56--68
%R 10.1609/aimag.v39i3.2816
%T On Reproducible AI: Towards Reproducible Research, Open Science, and Digital Scholarship in AI Publications
%V 39
%X Background: Science is experiencing a reproducibility crisis. Artificial intelligence research is not an exception. Objective: To give practical and pragmatic recommendations for how to document AI research so that the results are reproducible. Method: Our analysis of the literature shows that AI publications fall short of providing enough documentation to facilitate reproducibility. Our suggested best practices are based on a framework for reproducibility and recommendations given for other disciplines. Results: We have made an author checklist based on our investigation and provided examples for how every item in the checklist can be documented. Conclusion: We encourage reviewers to use the suggested best practices and author checklist when reviewing submissions for AAAI publications and future AAAI conferences.
@article{GundersenEtAl2018Reproducible,
abstract = {Background: Science is experiencing a reproducibility crisis. Artificial intelligence research is not an exception. Objective: To give practical and pragmatic recommendations for how to document AI research so that the results are reproducible. Method: Our analysis of the literature shows that AI publications fall short of providing enough documentation to facilitate reproducibility. Our suggested best practices are based on a framework for reproducibility and recommendations given for other disciplines. Results: We have made an author checklist based on our investigation and provided examples for how every item in the checklist can be documented. Conclusion: We encourage reviewers to use the suggested best practices and author checklist when reviewing submissions for AAAI publications and future AAAI conferences.},
added-at = {2024-01-29T00:47:34.000+0100},
author = {Gundersen, Odd Erik and Gil, Yolanda and Aha, David W.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2d85f5508661c71ad993d3a051501ab65/hermann},
copyright = {Copyright (c) AI Magazine},
doi = {10.1609/aimag.v39i3.2816},
file = {/Users/sibyllehermann/Zotero/storage/8DM3FNZL/Gundersen et al. - 2018 - On Reproducible AI Towards Reproducible Research,.pdf},
interhash = {9e9ce9e533be53204e4c655bef64dbe0},
intrahash = {d85f5508661c71ad993d3a051501ab65},
issn = {2371-9621},
journal = {AI Magazine},
keywords = {Diss},
langid = {english},
month = sep,
number = 3,
pages = {56--68},
shorttitle = {On {{Reproducible AI}}},
timestamp = {2024-01-29T00:47:34.000+0100},
title = {On {{Reproducible AI}}: {{Towards Reproducible Research}}, {{Open Science}}, and {{Digital Scholarship}} in {{AI Publications}}},
urldate = {2022-11-23},
volume = 39,
year = 2018
}
