Moral Machine Learning: Teaching a Course at the Intersection of Applied Statistics and Moral Philosophy

Statistical applications are increasingly inducing ethical considerations, which are often not able to be resolved via statistics alone. In this article, we present a proposed course that combines applied statistics and moral philosophy. The instructional methods included are designed with implementation at a large research institution in mind but are fully intended to be transferable to any setting adopting such an interdisciplinary course into its curriculum. The aforementioned methods will foreground case-studies as tangible examples in a recurrent workflow involving identification of a dilemma, statistical analysis, philosophical defense, and application to the particular case study. Formative and summative assessment mechanisms will be presented alongside future directions and potential pitfalls of such a course. Motivating the proposed course is a desire to fill the comparative void in moral reasoning for statistics and data science curricula.