Many data analytics projects are directly or indirectly centered around human subjects. Learning behavior of customers, students, employees, users, patients, and service providers are examples of direct data analytics of humans. Learning consumption of products, and usage of facilities indirectly relates to human behavior. Data analysis involving humans needs to be wary of unwanted social biases. The characteristics of datasets and the analytical tools used can fundamentally influence a model’s behavior. For a model to perform well and make meaningful contributions in the real world, its deployment context must match training or evaluation datasets. Failure to match context with datasets and machine learning techniques can have adverse effects in domains, such as criminal justice, human resource management, critical infrastructure, and finance. Consequences of mismatches include human suffering, loss of revenue or public relations setbacks. Explainable and ethical AI, Machine Learning and Data Analytics are gaining increasing importance among researchers and practitioners. This special session invites researchers to present their efforts related to the direct and indirect study of human behavior in data science in any domain including but not limited to health care, education, engineering, retail, and social media.