Human-Artificial Intelligence Collaboration For Knowledge Search And Content Quality: Architecture, Evaluation, And Governed Deployment
Keywords:
Human-AI Collaboration, Decision Support, Knowledge Search, Relevance Engineering, Content Quality, Offline Evaluation, Randomized Deployment, Drift Detection, Auditability.Abstract
This article presents a framework for human–artificial intelligence (AI) collaboration in knowledge search and content quality operations, addressing the structural limitations of both manual governance and unchecked automation. The key contributions are threefold: a layered candidate generation pipeline that converts behavioral signals into reviewable improvement proposals; a multi-stage evaluation architecture connecting offline quality measurement to live user outcomes; and a governance model integrating privacy safeguards, accountability structures, and audit infrastructure. Deployment evidence indicates that staged human-AI collaboration reduces critical errors and reallocates engineering effort toward strategic improvement. The framework is designed to be practically actionable, with each component mapped to a specific failure mode and measurable outcome.
