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

The application of the latest techniques from artificial intelligence (AI) and machine learning (ML) to improve and automate the decision-making required for solving real-world 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 NetAIbased solutions in their production networks. In Part I of this manifesto, we argue that to gain the operators' trust, researchers will have to pursue a more scientific approach towards NetAI than in the past that endeavors the development of explainable and generalizable learning models. In this paper, we go one step further and posit that this "opening up of NetAI research" will require that the largely self-assured hubris about NetAI gives way to a healthy dose humility. Rather than continuing to extol the virtues and "magic" of black-box models that largely obfuscate the critical role of the utilized data play in training these models, concerted research efforts will be needed to design NetAI-driven agents or systems that can be expected to perform well when deployed in production settings and are also required to exhibit strong robustness properties when faced with ambiguous situations and real-world uncertainties. We describe one such effort that is aimed at developing a new ML pipeline for generating trained models that strive to meet these expectations and requirements.