Artificial intelligence is increasingly integrated into decision-making. Previous research has shown that people tend to trust algorithmic advice more in objective tasks than in subjective ones. However, the trust levels in hybrid tasks, which involve both subjective and objective elements, remain unexplored, despite being more prevalent in real-world scenarios. This research aims to fill this gap by conducting online experiments to understand how trust varies in decisions made by algorithms, humans, and their collaboration across different task contexts: subjective, objective, and hybrid. It will also assess, within hybrid tasks, how the objectivity (or subjectivity) of tasks influences trust in human-AI collaboration. The findings are expected to offer significant implications for designing AI systems that effectively balance objective data and subjective judgment, thereby leading to better decision-making outcomes.