Fairness and bias are critical concerns in modern AI and social intelligence systems. This chapter first introduces the fundamental issues of demographic bias in data-driven social intelligence applications, such as facial analysis and educational assessment. We present two novel frameworks, FairCrowd and DebiasEdu to address these critical concerns. In particular, FairCrowd is a fair crowdsourcing-based data sampling framework that leverages crowd intelligence to infer demographic labels and achieve balanced dataset representation without requiring extensive manual annotations. DebiasEdu is a crowd-AI collaborative framework that combines gradient-based bias identification with crowd-guided bias calibration to achieve fair and accurate student performance prediction. Through comprehensive case studies on human face data sampling and student performance prediction, we demonstrate the effectiveness of these approaches to address fairness and bias issues in social intelligence and show the potential of integrating human intelligence with AI systems to create more equitable and effective social intelligence applications.