{"title":"模型报告,机器学习工程师和用户的监督工具","authors":"Amine Saboni, M. R. Ouamane, O. Bennis, F. Kratz","doi":"10.46300/9109.2022.16.5","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Model Reports, a Supervision Tool for Machine Learning Engineers and Users\",\"authors\":\"Amine Saboni, M. R. Ouamane, O. Bennis, F. Kratz\",\"doi\":\"10.46300/9109.2022.16.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2022-01-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46300/9109.2022.16.5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46300/9109.2022.16.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Model Reports, a Supervision Tool for Machine Learning Engineers and Users
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.