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Résumé : Abstract This chapter provides an integrative overview of computational approaches to Human–AI collaboration, highlighting the conceptual and ethical foundations necessary for effective and responsible interaction. We examine configurations of human and AI control, from human-in-the-loop to fully autonomous systems, and discuss how the nature of interaction—copresence, cooperation, and collaboration—shapes task performance. Bayesian models are presented as a formal framework for building and understanding human–AI collaboration, enabling intention recognition, shared representations, and mutual adaptation. Design implications and ethical considerations are emphasized, focusing on transparency, goal alignment, bias mitigation, adaptive mental/internal models, and the preservation of human oversight. We further explore practical applications, including legible robot motion generation for communicating intent and pedagogical reasoning for shared meaning-making in human–human collaborations. Throughout, we stress that effective collaboration requires dynamic mental/internal models, continuous learning, and alignment between human and AI goals, ensuring that AI systems enhance human decision-making while maintaining accountability and trust. By combining theoretical insights, computational models, and real-world applications, the chapter offers a foundation for students, researchers, and practitioners seeking to design human-centered, ethically responsible AI systems that support collaboration across diverse domains.