Margins and Opportunity

Shiva Kaul

Abstract: We use the statistical quantity of margin --- the distance between a decision boundary and a classified point, or the gap between two scores --- to formalize the principle of equal opportunity --- the chance to improve one's outcome, regardless of group status. This leads to a better definition of opportunity which recognizes, for example, that a strongly rejected individual was offered less recourse than a weakly rejected one, despite the shared outcome. It also leads to simpler algorithms, since real-valued margins are easier to analyze and optimize than discrete outcomes. We formalize two ways that a protected group may be guaranteed equal opportunity: (1) (social) mobility: acceptance should be within reach for the group (conversely, the general population shouldn't be cushioned from rejection), and (2) contrast: within the group, good candidates should get substantially higher scores than bad candidates, preventing the so-called 'token' effect. A simple linear classifier seems to offer roughly equal opportunity both experimentally and mathematically.

External IDs:dblp:conf/aies/Kaul18