Subgroup Fairness in Two-Sided Markets
- URL: http://arxiv.org/abs/2106.02702v1
- Date: Fri, 4 Jun 2021 20:26:16 GMT
- Title: Subgroup Fairness in Two-Sided Markets
- Authors: Quan Zhou and Jakub Marecek and Robert N. Shorten
- Abstract summary: We suggest a novel market-clearing mechanism for two-sided markets.
We show that a certain non-linear problem can be approximated to any subgroup in time.
On the example of driver-ride assignment in an Uber-like system, we demonstrate the efficacy and scalability of the approach.
- Score: 7.854010769859225
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It is well known that two-sided markets are unfair in a number of ways. For
instance, female workers at Uber earn less than their male colleagues per mile
driven. Similar observations have been made for other minority subgroups in
other two-sided markets. Here, we suggest a novel market-clearing mechanism for
two-sided markets, which promotes equalisation of the pay per hour worked
across multiple subgroups, as well as within each subgroup. In the process, we
introduce a novel notion of subgroup fairness (which we call Inter-fairness),
which can be combined with other notions of fairness within each subgroup
(called Intra-fairness), and the utility for the customers (Customer-Care) in
the objective of the market-clearing problem. While the novel non-linear terms
in the objective complicate market clearing by making the problem non-convex,
we show that a certain non-convex augmented Lagrangian relaxation can be
approximated to any precision in time polynomial in the number of market
participants using semi-definite programming. This makes it possible to
implement the market-clearing mechanism efficiently. On the example of
driver-ride assignment in an Uber-like system, we demonstrate the efficacy and
scalability of the approach, and trade-offs between Inter- and Intra-fairness.
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