Human-AI collaboration is a transformative approach in advancing AI-driven applications, combining artificial intelligence and human expertise to address complex problems. This chapter presents collaborative frameworks that enable the integration of human intelligence from crowdsourcing systems into the AI process, overcoming critical challenges such as large design search spaces and managing imperfect crowd-sourced data. Specifically, we present two case studies: (1) CrowdNAS, a crowd-guided neural architecture search framework for disaster damage assessment, which uses crowd inputs to identify optimal network architectures for accurate damage severity estimation, and (2) CrowdOptim, a crowd-driven hyperparameter optimization framework for AI-based smart urban sensing applications, which leverages crowdsourced feedback to enhance model performance in assessing urban environments. These frameworks and case studies demonstrate the power of collaborative frameworks in guiding AI optimization and neural architecture discovery, showing how crowd-AI systems can achieve high accuracy while reducing computational demands, paving the way for robust human-centered AI applications across dynamic and data-intensive environments.