DEPICT: A Framework for Ethical Reasoning for Statistics and Data Science

Abstract Number:

1474 

Submission Type:

Professional Development-Professional Skills Development (PSD) 

Participants:

Mario Davidson (1), Jennifer Van Mullekom (2)

Institutions:

(1) Vanderbilt University Medical Center, N/A, (2) Virginia Tech, N/A

Co-Instructor:

Jennifer Van Mullekom  
Virginia Tech

Primary Instructor:

Mario Davidson  
Vanderbilt University Medical Center

Description:

The recent revision of the American Statistical Association (ASA) Ethical Guidelines for Statistical Practice combined with increased media attention on ethical data science algorithms has prompted our profession to renew its commitment to ethics education. We have developed DEPICT, a six-phase ethical reasoning process tailored to statistics and data science. We will present overviews of ethics paradigms and the ASA Ethical Guidelines followed by a deep dive into the DEPICT process. Participants will Define ethical dilemmas; Explore possible resolutions; Plan resolutions; anticipate issues associated with Implementation; Contemplate their actions; and Transcend to incorporate key learnings to avoid future dilemmas. The course consists of interactive exercises to learn ethical frameworks, guidance, and reasoning followed by applications in complex, nuanced case studies. Attendees will participate in facilitated small group discussions as they apply the framework, reporting key elements of their small group discussion to the larger group. Participants will develop multi-perspective views and debate the pros and cons of various resolutions based on professional guidance. This course is appropriate for students, faculty, early career professionals, and managers in any application area. Participants will learn how to apply DEPICT as well as teach or mentor ethical reasoning in statistics and data science.

Instructor Background:

Dr. Mario Davidson is the Associate Vice Chair of Equity, Diversity, and Inclusion in the Department of Biostatistics at Vanderbilt University. He has taught the university's collaboration course which focuses on communication, professionalism, and ethics. He is the lead author of Teaching Communication in a Statistical Collaboration Course: A Feasible, Project-Based, Multi-Modal Curriculum which was published in The American Statistician. Dr. Jennifer Van Mullekom is the Director of the Statistical Applications and Innovations Group (SAIG) and a Professor of Statistical Practice at Virginia Tech. She teaches collaboration and communication skills as well as professionalism and ethics to students both in the classroom and through experiential learning in SAIG. Jen has co-authored and presented statistical ethics seminars sponsored by the VT Scholarly Integrity and Research Compliance office and required for all NSF researchers. Both instructors have presented this course at CSP.

Course Outline:

Topic 1-Ethical Paradigms: Discuss consequentialist, non-consequentialist, and agent-centered theories. Discuss paradigms in relation to some of the small group examples from the introduction.
Topic 2-Professional Ethical Guidance: Present a high level review of the ASA Ethical Guidelines. In small groups, brainstorm scenarios/experiences related to the principles of ASA Ethical Guidelines with each group dissecting one principle. The class discusses the exercise. In small groups, map scenarios to principles.
Topic 3-DEPICT Overview: Present the DEPICT model. Discuss the steps of DEPICT.
Topic 4-Case Study Analysis with Instructors Analyzing Borrowing Data without Permission and Students Analyzing Two Model Wrongs Don't Make a Right: Instructors discuss a component of DEPICT and students use the same component on their case. The class discusses the students' case with facilitation by the instructors.
Topic 5-Teaching and Faciliitating DEPICT: Advice and developing new cases.

Learning Outcomes:

By the end of this course, the participant should be able to:
• Discuss consequentialist, non-consequentialist, and agent-centered theories
• Apply ethical paradigmatic theories to dilemmas or situations
• Compare ethical paradigmatic approaches to a real-world situation
• Explain the importance of the ASA Ethical Guidelines for Statistical Practice
• Apply the principles of the ASA Ethical Guidelines for Statistical Practice to situations you may encounter within the profession
• Understand the steps of the DEPICT framework
• Apply DEPICT to analyze and resolve ethical dilemmas in case studies
• Relate DEPICT to ethical dilemma resolution in the workplace
• List challenges of introducing ethical theory
• Facilitate exercises using professional guidelines and DEPICT
• Develop cases
• Assess ethical reasoning

Ethical reasoning requires the development of competencies in the areas of knowledge, application,
communication, and metacognition. Attendees will increase their competencies in these areas as
described below:

Knowledge
K1. Explain ethical decision-making framework
K2. Demonstrate understanding of professional ethical guidance
K3. Name and describe paradigms and theories

Application
A1. Brainstorm elements of the dilemma and possible resolutions
A2. Gather and organize accurate information
A3. Analyze information to formulate ethical resolutions using systematic reasoning

Metacognition
M1. Appraise the application of the framework
M2. Create and compare an adaptable multi-perspective view of the dilemma and resolutions
M3. Decide in the context of ambiguity, multiplicity (multiple acceptable resolutions
supported by evidence) and relativism (resolutions are not absolute but depend on context)

Communication
C1. Select the appropriate audience for communication about an ethical issue
C2. Determine the appropriate language and methods of communication (e.g., informal
conversation, email, formal complaint/letter, virtual or in-person meeting, etc.)
C3. Plan and execute difficult conversations

Sponsors:

No Additional Sponsor 3
No Additional Sponsor 2
Committee on Professional Ethics 1

Do you need additional equipment for your course?

No

Length of Course (pick 1)

Full Day Course