56,788 research outputs found
Fair Off-Policy Learning from Observational Data
Algorithmic decision-making in practice must be fair for legal, ethical, and
societal reasons. To achieve this, prior research has contributed various
approaches that ensure fairness in machine learning predictions, while
comparatively little effort has focused on fairness in decision-making,
specifically off-policy learning. In this paper, we propose a novel framework
for fair off-policy learning: we learn decision rules from observational data
under different notions of fairness, where we explicitly assume that
observational data were collected under a different potentially discriminatory
behavioral policy. For this, we first formalize different fairness notions for
off-policy learning. We then propose a neural network-based framework to learn
optimal policies under different fairness notions. We further provide
theoretical guarantees in the form of generalization bounds for the
finite-sample version of our framework. We demonstrate the effectiveness of our
framework through extensive numerical experiments using both simulated and
real-world data. Altogether, our work enables algorithmic decision-making in a
wide array of practical applications where fairness must be ensured.Comment: Revised versio
- …