AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A productivity-oriented framework for evaluating human-AI collaboration as outcome quality relative to interaction cost is introduced and shows how dialogue analysis can inform the evaluation and design of AI systems.
The findings indicate that the proposed sub-group-specific bias mitigation is robust across different datasets, subgroups, and embeddings, demonstrating effectiveness in addressing intersectional biases in multimodal settings.
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