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Although numerous ethics courses are available, with many focusingspecifically on technology and computer ethics, pedagogical approaches employedin these courses rely exclusively on texts rather than on software developmentor data analysis. Technical students often consider these courses unimportantand a distraction from the "real" material. To develop instructional materialsand methodologies that are thoughtful and engaging, we must strive for balance:between texts and coding, between critique and solution, and betweencutting-edge research and practical applicability. Finding such balance isparticularly difficult in the nascent field of responsible data science (RDS),where we are only starting to understand how to interface between theintrinsically different methodologies of engineering and social sciences. Inthis paper we recount a recent experience in developing and teaching an RDScourse to graduate and advanced undergraduate students in data science. We thendive into an area that is critically important to RDS -- transparency andinterpretability of machine-assisted decision-making, and tie this area to theneeds of emerging RDS curricula. Recounting our own experience, and leveragingliterature on pedagogical methods in data science and beyond, we propose thenotion of an "object-to-interpret-with". We link this notion to "nutritionallabels" -- a family of interpretability tools that are gaining popularity inRDS research and practice. With this work we aim to contribute to the nascentarea of RDS education, and to inspire others in the community to come togetherto develop a deeper theoretical understanding of the pedagogical needs of RDS,and contribute concrete educational materials and methodologies that others canuse. All course materials are publicly available athttps://dataresponsibly.github.io/courses.

Teaching Responsible Data Science: Charting New Pedagogical Territory