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.
Before Class
Lectures
Readings
- Gold, Krinke, Ethical Mining
- Standing in the Fire: A Speculative Inquiry Into Meta-Relationality And Generative AI By Vanessa Machado De Oliveira and Aiden Cinnamon Tea
- Evren, Seren, “Who Owns the Code Claude Wrote?”
- Either read Doctorow’s “Car Wars” or watch a Black Mirror episode (or equivalent near-future dystopian piece).
In Class
Slides
Assignment 2, on Brightspace
Black Mirror exercise in class - see the templates for the exercise here and slide template here.
Optional Readings and Activities
- The Tuskegee Study
- Casey Fiesler’s twitter feed (and post that inspired this class)
- RetractionWatch
- https://theconvivialsociety.substack.com/p/the-questions-concerning-technology
- ACM Policies
- Does ACM’s code of ethics change decision making
- What we learned from NeurIPS 2020 reviewing process
- First Nations Information Governance Centre’s OCAP training