Over the course of the semester, we have engaged in structured thinking about data science ethics. You have had readings, class discussions, and written assignments that inform your thinking about data science ethics and challenge you to analyze and articulate the ethical implications of your work.

Much of the work we have done in data science ethics to date has focused on raising awareness, building comprehension of fundamental issues, application of acquired knowledge, and analysis. The highest level in Bloom’s taxonomy is evaluation.

In this essay assignment, you will evaluate actors in real-world situations where data science ethics are in play.

Learning goals

  • Assess the ethical implications to society of data-based research, analyses, and technology in an informed manner.
  • Use resources, such as professional guidelines, institutional review boards, and published research, to inform ethical responsibilities.

Content

Major readings

  • O’Neil (2016)
  • D’Ignazio and Klein (2020)
  • Bender et al. (2021)
  • Elliott, Stokes, and Cao (2018)
  • Washington and Kuo (2020)
  • Lum and Isaac (2016)
  • Angwin et al. (2016)

Assignment

You will write an essay on data science ethics, technology, and society (in R Markdown). In addition to the readings listed above, expect to do some additional research relevant to your topic. This is not a reflection or opinion paper – you should support your arguments with citations throughout.

Part 1: Choose a topic

Please read the description of the formal essay below before completing Part 1.

  • In one paragraph, describe what you want to write your ethics essay about.

Feedback will be quick and will either encourage you to proceed, or will redirect.

Common themes from the past

In previous capstones, students often drew on the following themes in their writing about data science ethics. This list is not exhaustive.

  • The search for a code of ethics for data science
  • What to do in the face of an ethical dilemma?
  • Knowing acceptance of the futility of protecting your private data online, especially since you started so young
  • Challenges of your job searches
  • Personal experience with ethical challenges at work
  • Could the widespread adoption of codes of ethics prevent unethical behavior in the future?

Part 2: Formal essay

In your essay, you should critically evaluate the actors in a real-world data science episode in which ethical considerations are salient.

For example, here is a generic essay prompt:

  • Choose one episode from D’Ignazio and Klein (2020) and analyze it in the context of the Data Values and Principles manifesto. Did the actors in this episode behave ethically? Explain why or why not by linking specific actions to specific ethical principles.

The chapter on data science ethics in Baumer, Kaplan, and Horton (2021) includes some short examples of this type of analysis.

A similar idea is:

  • Choose one episode from O’Neil (2016) and analyze it in the context of the Confucian principles ethical principles outlined by Elliott, Stokes, and Cao (2018). Did the actors in this episode behave ethically? Explain why or why not by linking specific actions to specific ethical principles.

Your essay should be 1000–1500 words in length, or about 3-5 pages.

Submission

Render your R Markdown document as HTML. Submit your HTML file to Moodle.

Rubric

Ethics essay rubric
Criteria Seven Eight Nine
Overall Quality No evaluation is made. Content is mostly description of the ethical dilemma. No title. Ideas are not clearly connected. No outside references. Many grammatical and/or formatting errors. Ethical dilemma is described, but no evaluation is made. Opinions or claims are unsubstantiated. Actors are clearly evaluated in reference to specific ethical principles. Structure of essay is clear. Ideas are clearly connected. Outside research is relevant, authoritative, and appropriately sourced. Formatting makes essay more readable.

References

Angwin, Julia, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. “Machine Bias.” ProPublica; ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
Baumer, Benjamin S., Daniel T. Kaplan, and Nicholas J. Horton. 2021. Modern Data Science with R. 2nd ed. Chapman; Hall/CRC Press: Boca Raton. https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498.
Bender, Emily M, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜.” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. https://doi.org/10.1145/3442188.3445922.
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.
Lum, Kristian, and William Isaac. 2016. “To Predict and Serve?” Significance 13 (5): 14–19. https://doi.org/10.1111/j.1740-9713.2016.00960.x.
O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. https://www.penguinrandomhouse.com/books/241363/weapons-of-math-destruction-by-cathy-oneil/.
Washington, Anne L, and Rachel Kuo. 2020. “Whose Side Are Ethics Codes on? Power, Responsibility and the Social Good.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 230–40. https://doi.org/10.1145/3351095.3372844.