AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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FairLint-DL, a Visual Studio Code extension that implements a shift-left approach to fairness testing by enabling pre-training, IDE-native bias detection directly on tabular datasets, demonstrates the feasibility of integrating fairness analysis into the developer workflow without significant overhead.
REMI, a framework for the automated localization, explanation, and mitigation of individual discrimination, is presented, which introduces a bidirectional relational explanation framework that learns over paired examples $(x, x')$ to identify regions of the input space where fairness is violated.
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