Ten simple rules for integrating ethics into statistics and data science instruction

• Activity Number: 551 - Balderdash, Codswallop, and Malarkey
• Type: Topic Contributed
• Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
• Sponsor: Section on Statistics and Data Science Education
• Author(s): William Cipolli* and Diane Evans* and David J Corliss* and Jonathan Auerbach* and Rochelle E Tractenberg* and Suzanne Thornton*
• Companies: Colgate University and Rose-Hulman Institute of Technology and Fiat Chrysler Automobiles (FCA) / Founder and Director, Peace-Work and Columbia University and Georgetown University Medical Center and Swarthmore College
• Keywords: ethical practice; Education; Data Science; statistics and data science; ASA Ethical Guidelines; teaching professional ethics

Abstract

Ten simple rules for integrating ethics content/training in ethical practice into every/any statistics and data science course are presented. These rules are intended to support instructors who seek to encourage ethical conduct in (throughout) the practice of science, whether it involves statistics, data science, or qualitative analysis; as well as throughout the employment of tools, methods, and techniques from these domains. Truly integrated ethical training can also promote awareness of the fact that every member of a research – or practice - team has a specific role, with attendant obligations and priorities, relating to the use of statistics, data science, and qualitative analytic approaches. Even if individuals are not going to be the ‘designated statistician/analyst’ on a project, understanding the roles and responsibilities of team members can strengthen the sense of responsibility and accountability of each member of a science or practice team. True integration of ethical training is not simple to achieve, but the ten rules are based on educational and cognitive sciences, as well as a recognition of the fact that additional content, without furthering a course’s existing learning objectives, greatly dampens enthusiasm for, and the likelihood of, integration of ethical training into quantitative courses. Assumptions for readers of these ten simple rules are: the instructor wants to have something that can be graded/evaluated after the students engage with the case; and that one objective the reader has is to teach how to reason & make decisions ethically as students go about practicing or using statistics. The overarching message of the ten rules is that true integration can benefit from leveraging existing structural features that both streamline learning outcomes and increase the chance of successfully embedding ethical practice standards into existing courses. Success is defined as the creation of reproducible, gradable work from students that signal whether or not the ethics instruction had its intended effects; and the documentation of ongoing (sustained) engagement with the ethics training beyond the end of the course.