Fairness in Resource Allocation and Slowed-down Dependent Rounding
Abstract
We consider an issue of much current concern: could fairness, an issue that is already difficult to guarantee, worsen when algorithms run much of our lives? We consider this in the context of resource-allocation problems, we show that algorithms can guarantee certain types of fairness in a verifiable way. Our conceptual contribution is a simple approach to fairness in this context, which only requires that all users trust some public lottery. Our technical contributions are in ways to address the -center and knapsack-center problems that arise in this context: we develop a novel dependent-rounding technique that, via the new ingredients of "slowing down" and additional randomization, guarantees stronger correlation properties than known before.
Cite
@article{arxiv.1704.06528,
title = {Fairness in Resource Allocation and Slowed-down Dependent Rounding},
author = {David G. Harris and Thomas Pensyl and Aravind Srinivasan and Khoa Trinh},
journal= {arXiv preprint arXiv:1704.06528},
year = {2017}
}
Comments
We decided to split these results to two separate papers. Please see arXiv:1709.06995