  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.
