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