Data ethics

“Data ethics” is too big of a topic for one page. See:

• Privacy and surveillance
• Algorithmic fairness
• Algorithmic due process
• Interpretable and explainable models
• Machine learning and law
• The many-hands and many-causes problems

This page will be reserved for discussions about the field of data ethics and how it is discussed.

• Colando, S., & Hardin, J. (2024). Philosophy within data science ethics courses. Journal of Statistics and Data Science Education. doi:10.1080/26939169.2024.2394542

  Surveys 16 data ethics courses from various universities to aggregate the topics they include. The most interesting part to me is Table 1: most of the courses talk about privacy, bias, and fairness, but very few of them talk about responsibility, justice, transparency, or consent. This aligns with my view that data ethics is too fixated on conventional ideas of privacy (where “privacy” means “concealing certain categories of information”) without consideration of its moral responsibility for its actions. Or things like due process, which is not mentioned at all in this review.