This book delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimized the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models; Diagnose bias and unfairness within models using multiple metrics; Audit projects to ensure fairness and minimize the possibility of unintended harm.