{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T01:10:22Z","timestamp":1755997822385,"version":"3.41.0"},"reference-count":5,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2023,9,28]]},"abstract":"<jats:p>The application of the latest techniques from artificial intelligence (AI) and machine learning (ML) to improve and automate the decision-making required for solving realworld network security and performance problems (NetAI, for short) has generated great excitement among networking researchers. However, network operators have remained very reluctant when it comes to deploying NetAI-based solutions in their production networks, mainly because the black-box nature of the underlying learning models forces operators to blindly trust these models without having any understanding of how they work, why they work, or when they don't work (and why not). Paraphrasing [1], we argue that to overcome this roadblock and ensure its future success in practice, NetAI \"has to get past its current stage of explorimentation, or the practice of poking around to see what happens, and has to start employing tools of the scientific method.\"<\/jats:p>","DOI":"10.1145\/3626570.3626609","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T22:16:57Z","timestamp":1696285017000},"page":"106-108","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A NetAI Manifesto (Part I): Less Explorimentation, More Science"],"prefix":"10.1145","volume":"51","author":[{"given":"Walter","family":"Willinger","sequence":"first","affiliation":[{"name":"NIKSUN, Inc"}]},{"given":"Arpit","family":"Gupta","sequence":"additional","affiliation":[{"name":"UCSB"}]},{"given":"Arthur S.","family":"Jacobs","sequence":"additional","affiliation":[{"name":"UFRGS"}]},{"given":"Roman","family":"Beltiukov","sequence":"additional","affiliation":[{"name":"UCSB"}]},{"given":"Ronaldo A.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"UFMS"}]},{"given":"Lisandro","family":"Granville","sequence":"additional","affiliation":[{"name":"UFRGS"}]}],"member":"320","published-online":{"date-parts":[[2023,10,2]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"The Scientific Method in the Science of Machine Learning. ICLR Debugging Machine Learning Models Workshop","author":"Forde J. Z.","year":"2019","unstructured":"J. Z. Forde and M. Paganini. The Scientific Method in the Science of Machine Learning. ICLR Debugging Machine Learning Models Workshop, 2019."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"e_1_2_1_3_1","first-page":"1","article-title":"Underspecification Presents Challenges for Credibility","volume":"23","author":"A.","year":"2022","unstructured":"A. D'Amor et. al. Underspecification Presents Challenges for Credibility in Modern Machine Learning. Journal of Machine Learning Research 23, 1--61, 2022.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560609"},{"key":"e_1_2_1_5_1","volume-title":"More Humility. Performance Evaluation Review, this issue","author":"Willinger W.","year":"2023","unstructured":"W. Willinger et al. A NetAI Manifesto (Part II): Less Hubris, More Humility. Performance Evaluation Review, this issue, 2023"}],"container-title":["ACM SIGMETRICS Performance Evaluation Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626570.3626609","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626570.3626609","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:45Z","timestamp":1750178205000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626570.3626609"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"references-count":5,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,9,28]]}},"alternative-id":["10.1145\/3626570.3626609"],"URL":"https:\/\/doi.org\/10.1145\/3626570.3626609","relation":{},"ISSN":["0163-5999"],"issn-type":[{"type":"print","value":"0163-5999"}],"subject":[],"published":{"date-parts":[[2023,9,28]]},"assertion":[{"value":"2023-10-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}