Ethical SE Data Science
Data mining, data science, and machine learning open up problematic (and interesting) research questions and obligations. We will go over some of the basic problems, some approaches to resolve those, and discuss current issues.
Learning Outcomes
- address concept of ethical problems in SE-oriented data science.
- identify choices that are more ethical rather than less.
- frame ethical dilemmas using existing frameworks.
| # | Topic | Readings | Exercises |
|---|---|---|---|
| 1-1 | Ethical Considerations in Data Science 4 SE | see below | see below |
Required Readings
- Gold, Krinke, Ethical Mining
- https://theconvivialsociety.substack.com/p/the-questions-concerning-technology
Optional Readings and Activities
- The Tuskegee Study
- Casey Fiesler’s twitter feed (and post that inspired this class)
- RetractionWatch
- ACM Policies
- Does ACM’s code of ethics change decision making
- What we learned from NeurIPS 2020 reviewing process
Exercises
- Assignment 2, on Brightspace
- Black Mirror exercise in class - see the templates for the exercise here and slide template here.