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arXiv · 1704.06528

Fairness in Resource Allocation and Slowed-down Dependent Rounding

David G. Harris·Thomas Pensyl·Aravind Srinivasan·Khoa Trinh
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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 $k$-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.

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David G. Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh. 2017-09-22. Fairness in Resource Allocation and Slowed-down Dependent Rounding. https://arxiv.org/abs/1704.06528

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