Explainability has been a critical aspect in social intelligence applications that analyze human-centered data and can directly impact human decision-making and well-being. This chapter presents two graph-based AI-driven explanation approaches, HC-COVID and DExFC, that address several fundamental challenges in developing explainable social intelligence systems. These challenges include the varying knowledge fact quality contributed by humans with diverse expertise, lack of modality-level annotations, and diverse cross-modal explanations. Through extensive experiments on real-world social intelligence case studies, including COVID-19 news truth discovery and fauxtography detection, both frameworks demonstrate significant performance gains in both prediction accuracy and explanation quality compared to state-of-the-art baselines.