  Notions of "fair classification" that have arisen in computer science
generally revolve around equalizing certain statistics across protected groups.
This approach has been criticized as ignoring societal issues, including how
errors can hurt certain groups disproportionately. We pose a modification of
one of the fairness criteria from Hardt, Price, and Srebro [NIPS, 2016] that
makes a small step towards addressing this issue in the case of financial
decisions like giving loans. We call this new notion "equalized financial
impact."
