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Artificial intelligence (AI) technologies are rapidly advancing, increasing concerns about data privacy harms in AI models. To this end, we examine how ethical AI can be incorporated into computer science curricula. This paper describes the design process for the first ‘AI Privacy Engineering' course, to the best of our knowledge, taught in the United States. The course is designed for both undergraduate and graduate students at the Georgia Institute of Technology. Throughout this course, students examine ethical implications of AI system design, development, deployment, and utilization. Recognizing that data privacy represents only one possible form of harm, the course blends theoretical and conceptual lectures with hands-on projects that require students to address ethical issues, including bias, fairness, and accountability in AI systems. Herein, we discuss the course design process, including selecting the appropriate body of knowledge, establishing learning objectives, creating assignments, and considering pedagogical methodologies we employed. We explain the empirical methods used to inform our design, including a systematic review of courses teaching AI development at over 40 universities. Additionally, we introduce a structured curriculum that can be used to effectively teach ethical and safe AI, and we propose how these topics may be incorporated more broadly into computer science courses. Finally, we discuss the early successes of the course, and the challenges faced while teaching it, particularly in maintaining relevance despite fast-paced changes in the field of AI, an evolving legislative landscape, accessing computational systems to run AI models, and varying levels of student preparedness.