Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours (1912.08388v2)

Abstract: Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that drivers may have over certain types of requests. We consider the case of peak hours when the demand for rides is more than the supply of drivers. Drivers are well aware of their advantage during the peak hours and can choose to be selective about which rides to accept. Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e.g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver. Such a system can be highly unfair to riders. However, increasing fairness might come at a cost of the overall profit made by the rideshare platform. To balance these conflicting goals, we present a flexible, non-adaptive algorithm, \lpalg, that allows the platform designer to control the profit and fairness of the system via parameters $\alpha$ and $\beta$ respectively. We model the matching problem as an online bipartite matching where the set of drivers is offline and requests arrive online. Upon the arrival of a request, we use \lpalg to assign it to a driver (the driver might then choose to accept or reject it) or reject the request. We formalize the measures of profit and fairness in our setting and show that by using \lpalg, the competitive ratios for profit and fairness measures would be no worse than $\alpha/e$ and $\beta/e$ respectively. Extensive experimental results on both real-world and synthetic datasets confirm the validity of our theoretical lower bounds. Additionally, they show that $\lpalg$ under some choice of $(\alpha, \beta)$ can beat two natural heuristics, Greedy and Uniform, on \emph{both} fairness and profit.

• The paper introduces the lpalg algorithm that integrates adjustable parameters α and β to optimize profit and fairness in rideshare services.
• It models the matching problem as an online bipartite scenario to ensure even treatment of ride requests during peak demand.
• Experimental results show lpalg outperforms Greedy and Uniform heuristics, validating its competitive ratios for both profit and fairness.

The paper "Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours" addresses a significant issue in rideshare platforms where, during peak demand times, traditional algorithms focusing solely on maximizing profit or minimizing rider wait time can exacerbate inherent biases. This bias can lead to certain types of requests, such as those from non-popular pick-up or drop-off locations, being systematically ignored.

Problem Context

During high-demand periods, there is often an imbalance wherein the number of ride requests far exceeds the number of available drivers. Drivers, aware of their advantageous position, can afford to be selective about which requests to accept. This selectivity can render the system unfair, as it might consistently overlook requests from less popular areas, leading to systemic bias and discrimination.

Objective

The authors aim to develop a method that balances the competing objectives of profit maximization and fairness. They introduce the algorithm \lpalg, which integrates two parameters, $\alpha$ and $\beta$, allowing platform designers to control the trade-offs between profit and fairness.

Methodology

The matching problem is modeled as an online bipartite matching problem, where:

• Drivers form the offline set.
• Requests form the online set.

Upon the arrival of a request, \lpalg determines whether to assign the request to a driver or reject it, taking into account the driver's choice to accept or reject the assigned request. The goal is to balance between:

• Profit, which is maximized by ensuring drivers are consistently engaged with high-fare requests.
• Fairness, which ensures equitable treatment of all requests, regardless of their type.

Theoretical Contributions

• Competitive Ratios: The algorithm guarantees that the competitive ratios for profit and fairness are no worse than $\alpha/e$ and $\beta/e$, respectively.
• Parameterization: By tuning parameters $\alpha$ (profit) and $\beta$ (fairness), the platform can strike the desired balance between the two objectives.

Experimental Results

The paper includes extensive experimental validation using both real-world and synthetic datasets. Key findings include:

• Beat Baseline Heuristics: \lpalg surpasses two common heuristics, Greedy and Uniform, both in terms of fairness and profit under certain configurations of $(\alpha, \beta)$.
• Validation of Theoretical Bounds: The experiments confirm the theoretical lower bounds for competitive ratios established by the algorithm.

Conclusion

The paper presents a novel, flexible algorithm that provides a practical solution to balancing profit and fairness in rideshare platforms. By enabling platform designers to adjust the parameters $\alpha$ and $\beta$, \lpalg ensures that the system can mitigate unfair biases while still remaining profitable. This dual objective alignment offers a significant step forward in the equitable operation of rideshare services during high-demand periods.