AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A comprehensive taxonomy of data-centric refactoring strategies is introduced, their application across ML-driven software projects is investigated, and their impact on model accuracy, robustness, maintainability, and reproducibility is evaluated.
It is suggested in the paper that a methodology of ethical governance based on principles of responsible AI should be structured, fairness-by-design, transparency, human-in-the-loop oversight, and constant impact assessment, which underscores the fact that AI systems have ethical failures that are seldom technical but rather socio-technical, which necessitate interventions at the policy, organizational governance, and technical design levels.
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