<doi_batch xmlns="http://www.crossref.org/schema/4.4.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.4.0"><head><doi_batch_id>fb392c8d-10a2-4f63-8731-3b51e0b35154</doi_batch_id><timestamp>20220114082719040</timestamp><depositor><depositor_name>naun:naun</depositor_name><email_address>mdt@crossref.org</email_address></depositor><registrant>MDT Deposit</registrant></head><body><journal><journal_metadata language="en"><full_title>International Journal of Education and Information Technologies</full_title><issn media_type="electronic">2074-1316</issn><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.46300/9109</doi><resource>http://www.naun.org/cms.action?id=3037</resource></doi_data></journal_metadata><journal_issue><publication_date media_type="online"><month>1</month><day>11</day><year>2022</year></publication_date><publication_date media_type="print"><month>1</month><day>11</day><year>2022</year></publication_date><journal_volume><volume>16</volume><doi_data><doi>10.46300/9109.2022.16</doi><resource>https://npublications.com/journals/educationinformation/2022.php</resource></doi_data></journal_volume></journal_issue><journal_article language="en"><titles><title>Model Reports, a Supervision Tool for Machine Learning Engineers and Users</title></titles><contributors><person_name sequence="first" contributor_role="author"><given_name>Amine</given_name><surname>Saboni</surname><affiliation>Data User-Centered Tribe OCTO Technology Paris, France</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Mohamed Ridha</given_name><surname>Ouamane</surname><affiliation>Department of Electrical Engineering PRISME Laboratory, INSA Centre Val de Loire Bourges, France</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Ouafae</given_name><surname>Bennis</surname><affiliation>Departement of Automatic PRISME Laboratory, University of Orleans Chartres, France</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Frédéric</given_name><surname>Kratz</surname><affiliation>Departement of Automatic PRISME Laboratory, , INSA Centre Val de Loire Bourges, France</affiliation></person_name></contributors><jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1"><jats:p>This article investigates a methodology to design an automated supervision report, ensuring the suitability between the designers and the users of an algorithm. For this purpose, we built a super-vision tool, focused on error diagnosis. The argumentation of the article relies first on the exposition of the reasons to use model reports as a supervision artefact, with a prototype of implementation at an organization level, describing the necessary tooling to industrialize its production. Finally, we propose a method for supervising machine learning algorithms in a responsible and sustainable way, starting from the conception of the algorithm, along its development and dur-ing its operating phase.</jats:p></jats:abstract><publication_date media_type="online"><month>1</month><day>14</day><year>2022</year></publication_date><publication_date media_type="print"><month>1</month><day>14</day><year>2022</year></publication_date><pages><first_page>50</first_page><last_page>54</last_page></pages><publisher_item><item_number item_number_type="article_number">5</item_number></publisher_item><ai:program xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" name="AccessIndicators"><ai:free_to_read start_date="2022-01-14"/><ai:license_ref applies_to="am" start_date="2022-01-14">https://npublications.com/journals/educationinformation/2022/a102008-005(2022).pdf</ai:license_ref></ai:program><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.46300/9109.2022.16.5</doi><resource>https://npublications.com/journals/educationinformation/2022/a102008-005(2022).pdf</resource></doi_data><citation_list><citation key="ref0"><doi>10.1145/3449081</doi><unstructured_citation>Rakova, B., Yang, J., Cramer, H., &amp; Chowdhury, R. (2021). Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices. Proceedings of the ACM on HumanComputer Interaction, 5(CSCW1), 1-23. </unstructured_citation></citation><citation key="ref1"><doi>10.1038/s42256-019-0088-2</doi><unstructured_citation>Jobin, A., Ienca, M., &amp; Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399 </unstructured_citation></citation><citation key="ref2"><unstructured_citation>Responsible AI widgets for Error Analysis, Microsoft Research </unstructured_citation></citation><citation key="ref3"><unstructured_citation>Shapash responsible AI tooling (2020), MAIF </unstructured_citation></citation><citation key="ref4"><unstructured_citation>Nushi, B., Kamar, E., &amp; Horvitz, E. (2018, June). Towards accountable ai: Hybrid human-machine analyses for characterizing system failure. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 6, No. 1) </unstructured_citation></citation><citation key="ref5"><doi>10.1145/3287560.3287596</doi><unstructured_citation>Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... &amp; Gebru, T. (2019, January). Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency (pp. 220-229) </unstructured_citation></citation><citation key="ref6"><unstructured_citation>Salesforce, Model Cards for ai model transparency (2020) https://blog.einstein.ai/model-cards-for-ai-model-transparency/ </unstructured_citation></citation><citation key="ref7"><unstructured_citation>People+AI Research Group, Google, user needs https://pair.withgoogle.com/chapter/user-needs/ </unstructured_citation></citation><citation key="ref8"><doi>10.1109/tpwrs.2006.873109</doi><unstructured_citation>Davidson, E. M., McArthur, S. D., McDonald, J. R., Cumming, T., &amp; Watt, I. (2006). Applying multi-agent system technology in practice: Automated management and analysis of SCADA and digital fault recorder data. IEEE Transactions on Power Systems, 21(2), 559-567 </unstructured_citation></citation><citation key="ref9"><unstructured_citation>Victor Schmidt, CodeCarbon: Estimate and Track Carbon Emissions from Machine Learning Computing, https://github.com/mlco2/codecarbon </unstructured_citation></citation><citation key="ref10"><unstructured_citation>Bourdon, A., Noureddine, A., Rouvoy, R., &amp; Seinturier, L. (2013). Powerapi: A software library to monitor the energy consumed at the process-level. ERCIM News, 2013(92). </unstructured_citation></citation><citation key="ref11"><doi>10.3389/fcomm.2020.598454</doi><unstructured_citation>Hoang, L. N. (2020). Science communication desperately needs more aligned recommendation algorithms. Frontiers in Communication, 5, 115 </unstructured_citation></citation><citation key="ref12"><unstructured_citation>Tournesol public wiki homepage, https://wiki.tournesol.app/index.php/Main_Page </unstructured_citation></citation><citation key="ref13"><doi>10.1145/3411764.3445188</doi><unstructured_citation>Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. (2021). Expanding Explainability: Towards Social Transparency in AI systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 82, 1–19. </unstructured_citation></citation><citation key="ref14"><unstructured_citation>Humble, J., &amp; Kim, G. (2018). Accelerate: The science of lean software and devops: Building and scaling high performing technology organizations. IT Revolution. </unstructured_citation></citation><citation key="ref15"><doi>10.1787/gov_glance-2013-graph4-fr</doi><unstructured_citation>Blangero, Grimprel, La confiance des utilisateurs dans les systèmes impliquant de l’IA, 2019 https://blog.octo.com/la-confiance-desutilisateurs-dans-les-systemes-impliquant-de-lintelligence-artificielle/ </unstructured_citation></citation><citation key="ref16"><doi>10.1109/wain52551.2021.00026</doi><unstructured_citation>Muccini, H., &amp; Vaidhyanathan, K. (2021). Software Architecture for ML-based Systems: What Exists and What Lies Ahead. arXiv preprint arXiv:2103.07950. </unstructured_citation></citation><citation key="ref17"><doi>10.1016/b978-2-294-76753-1.00115-6</doi><unstructured_citation>Lachheb, I. (2021) Le feature store, nouvel outil pour les projets de data science, https://blog.octo.com/le-feature-store-nouvel-outil-pourles-projets-data-science/ </unstructured_citation></citation><citation key="ref18"><doi>10.1145/3411764.3445518</doi><unstructured_citation>Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., &amp; Aroyo, L. M. (2021). " Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI. </unstructured_citation></citation><citation key="ref19"><unstructured_citation>Ait Bachir, S. (2021) Les tests automatisés en delivery de machine learning https://blog.octo.com/les-tests-automatises-en-delivery-demachine-learning/ </unstructured_citation></citation><citation key="ref20"><doi>10.1109/bigdata.2017.8258038</doi><unstructured_citation>Breck, E., Cai, S., Nielsen, E., Salib, M., &amp; Sculley, D. (2017, December). The ml test score: A rubric for ml production readiness and technical debt reduction. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 1123-1132). IEEE. </unstructured_citation></citation><citation key="ref21"><unstructured_citation>Histoire d’une architecture émergente, Compte-rendu du talk d’Emmanuel Lin Toulemonde, Duck Conf 2021, Alessandro Mosca. </unstructured_citation></citation><citation key="ref22"><unstructured_citation>Atelier de matrice d’erreur, publication in progress at https://blog.octo.com </unstructured_citation></citation><citation key="ref23"><doi>10.1609/aaai.v33i01.33012429</doi><unstructured_citation>Bansal, G., Nushi, B., Kamar, E., Weld, D. S., Lasecki, W. S., &amp; Horvitz, E. (2019, July). Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 2429-2437). </unstructured_citation></citation><citation key="ref24"><unstructured_citation>Kumar, I. E., Scheidegger, C., Venkatasubramanian, S., &amp; Friedler, S. (2020, January). Shapley Residuals: Quantifying the limits of the Shapley value for explanations. In ICML Workshop on Workshop on Human Interpretability in Machine Learning (WHI). </unstructured_citation></citation><citation key="ref25"><doi>10.37394/232018.2020.8.9</doi><unstructured_citation>Kara Combs, Mary Fendley, Trevor Bihl, A Preliminary Look at Heuristic Analysis for Assessing Artificial Intelligence Explainability, WSEAS Transaction on Computer Research, Volume 8, 2020, pp. 61- 72. </unstructured_citation></citation><citation key="ref26"><doi>10.1561/2200000083</doi><unstructured_citation>Peter Kairouz, H. Brendan McMahan, Brendan Avent, et al, Advances and open problems in federated learning, arXiv preprint arXiv:1912.04977, 2 </unstructured_citation></citation><citation key="ref27"><doi>10.1038/s41746-020-00323-1</doi><unstructured_citation>RIEKE, Nicola, HANCOX, Jonny, LI, Wenqi, et al. The future of digital health with federated learning. NPJ digital medicine, 2020, vol. 3, no 1, p. 1-7. </unstructured_citation></citation><citation key="ref28"><unstructured_citation>ASLETT, Louis JM, ESPERANÇA, Pedro M., et HOLMES, Chris C. A review of homomorphic encryption and software tools for encrypted statistical machine learning. arXiv preprint arXiv:1508.06574, 2015. </unstructured_citation></citation><citation key="ref29"><doi>10.4000/books.pressesenssib.4488</doi><unstructured_citation>Roussel F., Pemodjo M., Saboni A. L’atelier matrice d’erreur, démystifier les performances du ML avec ses utilisateurs, https://blog.octo.com/latelier-matrice-derreur-demystifier-lesperformances-du-ml-avec-ses-utilisateurs/</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>