Teaching at the Intersection of Social Justice, Ethics, and the ASA Ethical Guidelines for Statistical Practice

Case studies are typically used to teach 'ethics', but when the content of a course is focused on formulae and proofs, a case analysis and the knowledge, skills, and abilities they require can be distracting. Moreover, case analyses are typically focused narrowly on research issues: obtaining consent, dealing with research team members, and/or research policy violations. Not all students in quantitative courses plan to become researchers, and ethical practice of mathematics, statistics, data science, and computing is an essential topic regardless of the learner's career plans. While it is incorrect to treat 'social justice' as a proxy for 'ethical practice', the topic of 'social justice' may be more interesting to both students and instructors. This paper offers concrete recommendations for integrating social justice content into quantitative courses in ways that limit the burden of new knowledge, skills, and abilities but also support reproducible and actionable assessments. Five tools can be utilized to integrate social justice into a course in a way that also meets calls to integrate 'ethics'; minimizes the burden on instructors to create and grade new materials and assignments; minimizes the burden on learners to develop the skill set to complete a case analysis; and maximizes the likelihood that the ethics content will be embedded in the learners' cognitive representation of the knowledge being taught in the quantitative course. These tools are: a. Curriculum Development Guidelines b. 7-task Statistics and Data Science Pipeline c. ASA Ethical Guidelines for Statistical Practice d. Stakeholder Analysis e. 6-step Ethical Reasoning paradigm This paper discusses how to use these tools in quantitative courses. The tools and frameworks offer structure, and facilitate ensuring that changes made to any course are evaluable and generate actionable assessments for learners.