Machine Learning and AI Systems excludes the tail ends of the distributions. Synthetic/generative/federated models suck at these. And, for a lot of industries, the most interesting cases are outliers (esp. in medical AI)

This is where minorities live, and the result is that most ML systems end up reproducing existing systems of power (re: To live in their Utopia, Matthew Effect)


Does “not trying to overfit” mean we perform badly on some groups?

  • If you have 99% “Group A” in your dataset, model can do well on average by only focusing on Group A
  • Treat the other 1% as outliers
  • Doing well at test-time might mean ignoring outliers and minorities

Contextual Data

Should data and information be contextualized all the time?

  1. Context is important when dealing with historical data. Knowing why certain decisions were made is extremely important
  2. We want data to be anonymized to a certain extent. Exposing patient data, for example, is a huge risk.

How do we choose what context to include and what not to include?