What happens when AI meets people? How can we ensure that AI results are:
Correct,
Just, and
Useful?
The first two are the subject of a subfield called Fairness, Accountability, and Transparency; the last is the subject of much research in human-computer interaction (HCI) and computer-supported cooperative work (CSCW). We’ll explore all three in these last two weeks of class.
We’ll start this week with how we might convince ourselves that model outputs are (or aren’t) correct.
Preparation
Correctness and Transparency / Explainability
Read one or more of these:
Explainable AI Guide (“Your high-level guide to the set of tools and methods that helps humans understand AI/ML models and their predictions.”)
Interpreting Neural Nets: Skim one of these articles:
Many unwittingly used a data set that contained chest scans of children who did not have covid as their examples of what non-covid cases looked like. But as a result, the AIs learned to identify kids, not covid.