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Thanks for opening up this discussion. I agree strongly that AI models will benefit from the kinds of metadata explored by the CODATA community, and that our work on metadata—and particularly data FAIRness—will be enhanced by taking advantage of methods developed
by the AI community in its early work on knowledge modeling. There is tremendous synergy here that needs to be exploited as we try to capture in metadata the essence of the content of digital research objects.
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<div>Here is a <a href="https://www.nature.com/articles/s41597-022-01815-3">link</a> to a recent paper that discusses some of these issues.</div>
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<div>Mark<br>
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Mark A. Musen, M.D., Ph.D.<br>
Stanford Medicine Professor of Biomedical Informatics Research</div>
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Professor of Medicine and Biomedical Data Science</div>
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Head, Stanford Center for Biomedical Informatics Research<br>
Stanford University School of Medicine, Mail Code: 5702<br>
3180 Porter Drive, Room B106<br>
Palo Alto, CA 94304<br>
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Phone: +1 (650) 725-3390<br>
e-mail: musen@stanford.edu<br>
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<div>On Apr 25, 2023, at 12:10 PM, Matt Jones <jones@nceas.ucsb.edu> wrote:</div>
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<div>Thanks, that was a nice post, I enjoyed it. It struck me as related to the FARR project, which is trying to tackle a similar set of issues as those you outline:</div>
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<div>FARR: FAIR in ML, AI Readiness, & Reproducibility (<a href="https://www.gofair.us/farr">https://www.gofair.us/farr</a>)</div>
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<div>They published a recent case study on publishing AI models in physics, and are working on other disciplinary issues as well:</div>
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<div class="gmail-csl-entry">Duarte J, Li H, Roy A, Zhu R, Huerta EA, Diaz D, Harris P, Kansal R, Katz DS, Kavoori IH, Kindratenko VV, Mokhtar F, Neubauer MS, Park SE, Quinnan M, Rusack R, Zhao Z (2022) FAIR AI Models in High Energy Physics.
<a href="https://doi.org/10.48550/arXiv.2212.05081">https://doi.org/10.48550/arXiv.2212.05081</a></div>
<span class="gmail-Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_id=info%3Adoi%2F10.48550%2FarXiv.2212.05081&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.type=preprint&rft.title=FAIR%20AI%20Models%20in%20High%20Energy%20Physics&rft.description=The%20findable%2C%20accessible%2C%20interoperable%2C%20and%20reusable%20(FAIR)%20data%20principles%20have%20provided%20a%20framework%20for%20examining%2C%20evaluating%2C%20and%20improving%20how%20we%20share%20data%20with%20the%20aim%20of%20facilitating%20scientific%20discovery.%20Efforts%20have%20been%20made%20to%20generalize%20these%20principles%20to%20research%20software%20and%20other%20digital%20products.%20Artificial%20intelligence%20(AI)%20models%20--%20algorithms%20that%20have%20been%20trained%20on%20data%20rather%20than%20explicitly%20programmed%20--%20are%20an%20important%20target%20for%20this%20because%20of%20the%20ever-increasing%20pace%20with%20which%20AI%20is%20transforming%20scientific%20and%20engineering%20domains.%20In%20this%20paper%2C%20we%20propose%20a%20practical%20definition%20of%20FAIR%20principles%20for%20AI%20models%20and%20create%20a%20FAIR%20AI%20project%20template%20that%20promotes%20adherence%20to%20these%20principles.%20We%20demonstrate%20how%20to%20implement%20these%20principles%20using%20a%20concrete%20example%20from%20experimental%20high%20energy%20physics%3A%20a%20graph%20neural%20network%20for%20identifying%20Higgs%20bosons%20decaying%20to%20bottom%20quarks.%20We%20study%20the%20robustness%20of%20these%20FAIR%20AI%20models%20and%20their%20portability%20across%20hardware%20architectures%20and%20software%20frameworks%2C%20and%20report%20new%20insights%20on%20the%20interpretability%20of%20AI%20predictions%20by%20studying%20the%20interplay%20between%20FAIR%20datasets%20and%20AI%20models.%20Enabled%20by%20publishing%20FAIR%20AI%20models%2C%20these%20studies%20pave%20the%20way%20toward%20reliable%20and%20automated%20AI-driven%20scientific%20discovery.&rft.identifier=urn%3Adoi%3A10.48550%2FarXiv.2212.05081&rft.aufirst=Javier&rft.aulast=Duarte&rft.au=Javier%20Duarte&rft.au=Haoyang%20Li&rft.au=Avik%20Roy&rft.au=Ruike%20Zhu&rft.au=E.%20A.%20Huerta&rft.au=Daniel%20Diaz&rft.au=Philip%20Harris&rft.au=Raghav%20Kansal&rft.au=Daniel%20S.%20Katz&rft.au=Ishaan%20H.%20Kavoori&rft.au=Volodymyr%20V.%20Kindratenko&rft.au=Farouk%20Mokhtar&rft.au=Mark%20S.%20Neubauer&rft.au=Sang%20Eon%20Park&rft.au=Melissa%20Quinnan&rft.au=Roger%20Rusack&rft.au=Zhizhen%20Zhao&rft.date=2022-12-21"></span><br>
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<div class="gmail-csl-bib-body" style="line-height:1.35">Matt<br>
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<div><b>Matthew B. Jones</b></div>
<div>ORCID: <a href="https://orcid.org/0000-0003-0077-4738" target="_blank">0000-0003-0077-4738</a></div>
<div>Director of Informatics R&D, <a href="http://www.nceas.ucsb.edu/ecoinfo" style="color:rgb(17,85,204)" target="_blank">National Center for Ecological Analysis and Synthesis</a></div>
<div>PI, NSF <a href="https://arcticdata.io/" style="color:rgb(17,85,204)" target="_blank">Arctic Data Center</a></div>
<div>Director, <a href="https://dataone.org/" style="color:rgb(17,85,204)" target="_blank">DataONE</a> program
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<div>University of California Santa Barbara</div>
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<div dir="ltr" class="gmail_attr">On Tue, Apr 25, 2023 at 7:01 AM Pascal Heus <<a href="mailto:pascal.heus@postman.com">pascal.heus@postman.com</a>> wrote:<br>
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<div dir="ltr">Dear colleagues:
<div>I just published this short article around AI transparency and metadata challenges which might be of interest to you. One of the objectives is to foster a bridge between the AI and (meta)data communities.</div>
<div><a href="https://plgah.medium.com/ai-has-a-metadata-problem-78b30ca1936b" target="_blank">https://plgah.medium.com/ai-has-a-metadata-problem-78b30ca1936b</a><br>
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<div>Thoughts, feedback, suggestions most appreciated.</div>
<div>Best,</div>
<div>*P</div>
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