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Journal of Statistics and Data Science Education
Article . 2025 . Peer-reviewed
License: CC BY
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Article . 2025
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Moral Machine Learning: Teaching a Course at the Intersection of Applied Statistics and Moral Philosophy

Authors: Andrew Ackerman;

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

Abstract

Statistical applications are increasingly inducing ethical considerations, which are often not able to be resolved via statistics alone. In this paper, 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.

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Keywords

Interdisciplinary, LC8-6691, Case-study, Probabilities. Mathematical statistics, Special aspects of education, QA273-280, Data ethics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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