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Ethics have become an urgent concern for data science research, practice, andinstruction in the wake of growing critique of algorithms and systems showingthat they reinforce structural oppression. There has been increasing desire onthe part of data science educators to craft curricula that speak to thesecritiques, yet much ethics education remains individualized, focused onspecific cases, or too abstract and unapplicable. We synthesized some of themost popular critical data science works and designed a data science ethicscourse that spoke to the social phenomena at the root of critical data studies-- theories of oppression, social systems, power, history, and change --through analysis of a pressing sociotechnical system: surveillance systems.Through analysis of student reflections and final projects, we determined thatat the conclusion of the semester, all students had developed critical analysisskills that allowed them to investigate surveillance systems of their own andidentify their benefits, harms, main proponents, those who resist them, andtheir interplay with social systems, all while considering dimensions of race,class, gender, and more. We argue that this type of instruction -- directlyteaching data science ethics alongside social theory -- is a crucial next stepfor the field.

Beyond case studies: Teaching data science critique and ethics through sociotechnical surveillance studies