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A NetAI Manifesto (Part I): Less Explorimentation, More Science

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Lisandro GranvilleAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 51, Issue 2

Pages 106 - 108

https://doi.org/10.1145/3626570.3626609

Published: 02 October 2023 Publication History

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Abstract

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."

References

[1]

J. Z. Forde and M. Paganini. The Scientific Method in the Science of Machine Learning. ICLR Debugging Machine Learning Models Workshop, 2019.

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[2]

C. Rudin. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell 1, 206--215, 2019.

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[3]

A. D'Amor et. al. Underspecification Presents Challenges for Credibility in Modern Machine Learning. Journal of Machine Learning Research 23, 1--61, 2022.

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[4]

A. S. Jacobs et al.AI/ML for network security: The emperor has no clothes. Proc. ACM CCS'22, 2022.

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[5]

W. Willinger et al. A NetAI Manifesto (Part II): Less Hubris, More Humility. Performance Evaluation Review, this issue, 2023

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cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 51, Issue 2

September 2023

110 pages

ISSN:0163-5999

DOI:10.1145/3626570

Editor:
Bo Ji
Virginia Tech

Issue’s Table of Contents

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Association for Computing Machinery

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Published: 02 October 2023

Published in SIGMETRICS Volume 51, Issue 2

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A NetAI Manifesto (Part II): Less Hubris, more Humility

A manifesto on explainability for artificial intelligence in medicine

Two decades of network science: as seen through the co-authorship network of network scientists

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