  Although numerous ethics courses are available, with many focusing
specifically on technology and computer ethics, pedagogical approaches employed
in these courses rely exclusively on texts rather than on software development
or data analysis. Technical students often consider these courses unimportant
and a distraction from the "real" material. To develop instructional materials
and methodologies that are thoughtful and engaging, we must strive for balance:
between texts and coding, between critique and solution, and between
cutting-edge research and practical applicability. Finding such balance is
particularly difficult in the nascent field of responsible data science (RDS),
where we are only starting to understand how to interface between the
intrinsically different methodologies of engineering and social sciences. In
this paper we recount a recent experience in developing and teaching an RDS
course to graduate and advanced undergraduate students in data science. We then
dive into an area that is critically important to RDS -- transparency and
interpretability of machine-assisted decision-making, and tie this area to the
needs of emerging RDS curricula. Recounting our own experience, and leveraging
literature on pedagogical methods in data science and beyond, we propose the
notion of an "object-to-interpret-with". We link this notion to "nutritional
labels" -- a family of interpretability tools that are gaining popularity in
RDS research and practice. With this work we aim to contribute to the nascent
area of RDS education, and to inspire others in the community to come together
to develop a deeper theoretical understanding of the pedagogical needs of RDS,
and contribute concrete educational materials and methodologies that others can
use. All course materials are publicly available at
https://dataresponsibly.github.io/courses.
