Lab 11: Ethical Codes for Data Science

Why are we here?

Two weeks ago, we discussed the issues that can arise from computational work when one “focuses on just the data science”. Today, we will discuss ethical codes that various professional societies have proposed, in order to prevent and mitigate negative consequences from unethical practices. This lab is structured differently than many previous ones, because we will be spending half an hour in a structured group discussion, follow by time to work on Phase Four of the final project.

Lab Goals

The goal of today is to make progress in defining practical steps to engage in more ethical data science and statistical work.

After completing this lab, you should be able to identify practices and procedures for engaging in computational work that is more ethical, to compare and contrast different methods for working with data through an ethical lens as described by one of the statements of ethical guidelines.

Required Reading

Leading up to this week, you engaged with the following materials on reproducibility and ethical codes:

You also read one of the following:

Lab instructions

Today will be a structured discussion during the first part of class. Unless you need to reference a reading, please close your laptop and focus on the discussion. There will be time afterwards for your next project rotation.

Question

Give your groupmates a summary of the article you read. What did it cover? What were your main takeaways?

Next, your group will discuss the following three questions for 15–20 minutes.

Question

What surprised you most about these readings?

Question

What frustrated you most about these readings?

Question

What suggestions do you have for processes and procedures to avoid, mitigate, or lessen bias and differential impact from data science on the world?

Reporting back

Then we will come together as a class for a short conversation before transitioning to individual work on the final project.

In your group, pick one person to share out for each question and agree on what will be said for each question. You may find it helpful to jot down notes.

We’ll spend another 10–15 minutes reporting back to the class about what we discussed in our groups.

One minute essay

Finally, spend one minute writing down an answer to the following question:

Question

What came up during the wrap up session that was new to you? In other words, what points were discussed in the wrap up that were not part of your discussion?

Next Steps

Step 1: Complete the reflection on Moodle

Write a reflection on the readings and discussion. Save it locally, and then paste it in the Moodle question. Please write at least 150 words. Submissions that are on topic and at least 150 words will receive credit. This counts towards your Weekly Lab Quizzes token.

Questions to consider:

  • What surprised or frustrated you about the readings?

  • Do you have recommendations for how to mitigate potential bias in the practice of statistics and data science?

  • What questions might you ask of yourself, the people you work with, or your data to encourage responsible use of data?

  • How has your perspective changed through the course of the readings and discussion?

Step 2: Final Project Phase 4

At this point in time, you should have:

  1. Completed your work on Phase 3 of the project
  2. Turned in that work on Moodle, and sent all your material to the next person in the rotation schedule
  3. Received completed Phase 3 materials from your groupmate.

During this week, you will complete Phase 4 of the final project. See the project page for details about what is expected during this phase of the project.

Step 3: Optional reading

To prepare for next week’s lab on citations and BibTeX, we encourage reading the following:

References

Drum, Kevin. 2013. “It’s the Austerity, Stupid: How We Were Sold an Economy-Killing Lie.” Mother Jones. https://www.motherjones.com/politics/2013/09/austerity-reinhart-rogoff-stimulus-debt-ceiling/.
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.
“Fact Sheet: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence.” 2023. The White House. https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/.