In this talk we survey some formulations of fairness requirements for decision making under uncertainty. We then discuss results from 3 recent papers:

1) Treating individuals fairly is not in conflict with long-term scientific learning goals if the population is sufficiently diverse.

2) When there is a pipeline of decisions, end-to-end fairness is impossible to achieve even in a very simple model.

3) Exploiting the knowledge acquired by others can unfairly advantage the free rider.

These papers are joint work with a number of co-authors:

Christopher Jung, Neil Lutz, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Steven Wu, and Juba Ziani