Codes of Ethics

Learning goals

After completing this assignment, students should be able to:

• Name at least two different written set of ethical principles
• Compare and contrast at least two codes of ethics

Content

• Read (mandatory):
  • Everyone: Data Values and Principles
  • chicubs: Hippocratic Oath for Data Science: Ch. 13/Appendix D (National Academies of Sciences 2018) and Hippocratic Oath
  • elmhurst: FAIR principles (Wilkinson et al. 2016)
  • dancedata: CARE principles (Carroll et al. 2020)
  • honda: Ethical Guidelines for Statistical Practice (Committee on Professional Ethics 2022)
  • cleveland: Introduction to Data Feminism (D’Ignazio and Klein 2020)
  • fhi: GSK Group AI Ethics
• Supplementary reading (optional):
  • Data for Good Exchange
  • Commentary by Tom Simonite
  • Commentary by Virgina Eubanks
  • Comparison by Lori Sherer
  • Interview with Cathy O’Neil
  • Elliott, Stokes, and Cao (2018)

In-class activity

• (10 mins): Discuss what you read in your group. Make sure that you understand your code of ethics.
• (20 mins): Prepare to present your code of ethics to another group (that hasn’t read them).
• (15 mins): Advocate for your code of ethics relative to the other group’s. What are its advantages?
• (20 mins): Report the results of your debate back to the class.

Assignment

Respond to the following prompt in writing.

• In one paragraph, compare and contrast the two codes of ethics that you learned about. What do they have in common? How are they different? What is present in one but not the other?

References

Carroll, Stephanie Russo, Ibrahim Garba, Oscar L Figueroa-Rodrı́guez, Jarita Holbrook, Raymond Lovett, Simeon Materechera, Mark Parsons, et al. 2020. “The CARE Principles for Indigenous Data Governance.” Data Science Journal 19 (1). https://doi.org/10.5334/dsj-2020-043.

Committee on Professional Ethics. 2022. “Ethical Guidelines for Statistical Practice.” American Statistical Association. https://www.amstat.org/docs/default-source/amstat-documents/ethicalguidelines.pdf.

D’Ignazio, Catherine, and Lauren F Klein. 2020. Data Feminism. MIT Press. https://mitpress.mit.edu/books/data-feminism.

Elliott, Alan C, S Lynne Stokes, and Jing Cao. 2018. “Teaching Ethics in a Statistics Curriculum with a Cross-Cultural Emphasis.” The American Statistician 72 (4): 359–67. https://doi.org/10.1080/00031305.2017.1307140.

National Academies of Sciences. 2018. Data Science for Undergraduates: Opportunities and Options. National Academies Press.

Wilkinson, Mark D, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (1): 1–9. https://doi.org/10.1038/sdata.2016.18.