An Algorithmic Framework for Positive Action

Abstract

Positive action is defined within anti-discrimination legislation as voluntary, legal action taken to address an imbalance of opportunity affecting individuals belonging to under-represented groups. Within this theme, we propose a novel algorithmic fairness framework to advance equal representation while respecting anti-discrimination legislation and equal-treatment rights. We use a counterfactual fairness approach to assign one of three outcomes to each candidate: accept; reject; or flagged as a positive action candidate.