With the ongoing explosive growth of AI/ML models and systems, Krishnaram explores some of the ethical, legal, and technical challenges that researchers and practitioners alike encounter. He discusses the need for adopting a fairness and privacy by design approach when developing AI/ML models and systems for different consumer and enterprise applications. Then she focuses on the application of fairness-aware machine learning and privacy-preserving data-mining techniques in practice by presenting case studies spanning different LinkedIn applications, such as fairness-aware talent search ranking, privacy-preserving analytics, and LinkedIn salary privacy and security design.