  Several scientific studies have reported the existence of the income gap
among rideshare drivers based on demographic factors such as gender, age, race,
etc. In this paper, we study the income inequality among rideshare drivers due
to discriminative cancellations from riders, and the tradeoff between the
income inequality (called fairness objective) with the system efficiency
(called profit objective). We proposed an online bipartite-matching model where
riders are assumed to arrive sequentially following a distribution known in
advance. The highlight of our model is the concept of acceptance rate between
any pair of driver-rider types, where types are defined based on demographic
factors. Specially, we assume each rider can accept or cancel the driver
assigned to her, each occurs with a certain probability which reflects the
acceptance degree from the rider type towards the driver type. We construct a
bi-objective linear program as a valid benchmark and propose two LP-based
parameterized online algorithms. Rigorous online competitive ratio analysis is
offered to demonstrate the flexibility and efficiency of our online algorithms
in balancing the two conflicting goals, promotions of fairness and profit.
Experimental results on a real-world dataset are provided as well, which
confirm our theoretical predictions.
