Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling

TL;DR

This paper explores data-driven approaches to balance fairness and efficiency in ride-pooling platforms by incorporating fairness constraints and income redistribution, demonstrating improved equity without sacrificing profitability.

Contribution

It introduces two novel methods: fairness-aware optimization and income redistribution, to reduce inequality in ride-pooling platforms, validated using NYC taxi data.

Findings

Fairness-aware optimization improves rider service in disadvantaged neighborhoods.

Income redistribution aligns driver earnings with fairness goals while maintaining incentives.

Both methods enhance fairness without harming platform profitability.

Abstract

Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce inequality either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: (1) incorporating fairness constraints into the objective function and (2) redistributing income to drivers to reduce income fluctuation and inequality. To evaluate our solutions, we use the New York City taxi data set. For the first method, we find that optimizing for driver-side fairness outperforms state-of-the-art models on the number of riders serviced, both in the worst-off neighborhood and overall, showing that optimizing for fairness can assist profitability in certain circumstances. For the second method, we explore income redistribution as a way to combat income inequality by having drivers keep an $r$ fraction of their income, and contributing the rest to a redistribution pool. For certain values of $r$, most drivers earn near their Shapley value, while still incentivizing drivers to maximize value, thereby avoiding the free-rider problem and reducing income variability. The first method can be extended to many definitions of fairness and the second method provably improves fairness without affecting profitability.