Chapter 11 completes the discussion of Chapter 10 by raising the question of how to practice machine learning in a responsible manner. It describes the dangers of data bias, and surveys data bias detection and mitigation methods; it lists the benefits of explainability and discusses techniques, such as LIME and SHAP, that have been proposed to explain the decisions made by opaque models; it underlines the risks of discrimination and discusses how to enhance fairness and prevent discrimination in machine learning algorithms. The issues of privacy and security are then presented, and the need to practice human-centered machine learning emphasized. The chapter concludes with the important issues of repeatability, reproducibility, and replicability in machine learning.