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Résumé : In today’s data-driven world, the demand for advanced intelligent systems to automate and enhance complex tasks is growing. However, developing effective artificial intelligence (AI) often depends on extensive, high-quality training data, which can be costly and time-consuming to obtain. This chapter highlights the potential of human–AI collaboration by integrating human expertise into machine learning workflows to address data limitations and enhance model performance. We explore foundational concepts such as Human-in-the-Loop systems, Active Learning, Crowdsourcing, and Interactive Machine Learning, outlining their interconnections as key paradigms. Through practical applications in diverse domains such as healthcare, finance, and agriculture, along with real-world case studies in education and law, we demonstrate how strategically incorporating human expertise into machine learning workflows can significantly enhance AI performance. From an information science perspective, this chapter emphasizes the powerful human–AI partnership that can drive the next generation of AI systems, enabling continuous learning from human experts and advancing capability and performance.