# Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems

arXiv: https://arxiv.org/abs/2303.14332 · HTML: https://mlsanity.com/paper/2303.14332/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019)
- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours](https://mlsanity.com/paper/1912.08388/) (2019)
- [Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling](https://mlsanity.com/paper/2110.03524/) (2021)
- [Trading the System Efficiency for the Income Equality of Drivers in Rideshare](https://mlsanity.com/paper/2012.06850/) (2020)
- [Zone pAth Construction (ZAC) based Approaches for Effective Real-Time Ridesharing](https://mlsanity.com/paper/2009.06051/) (2020)
- [Large Scale Real-time Ridesharing with Service Guarantee on Road Networks](https://mlsanity.com/paper/1302.6666/) (2013)
- [The Costs and Benefits of Sharing: Sequential Individual Rationality and Sequential Fairness](https://mlsanity.com/paper/1607.07306/) (2016)

## Built on by (most cited first, 5 of 5)

- [DECAF: Learning to be Fair in Multi-agent Resource Allocation](https://mlsanity.com/paper/2502.04281/) (2025)
- [Past-Discounting is Key for Learning Markovian Fairness with Long Horizons](https://mlsanity.com/paper/2504.01154/) (2025)
- [Long-term Fairness in Ride-Hailing Platform](https://mlsanity.com/paper/2407.17839/) (2024)
- [A General Incentives-Based Framework for Fairness in Multi-agent Resource Allocation](https://mlsanity.com/paper/2510.26740/) (2025)
- [Mind the Gaps: Auditing and Reducing Group Inequity in Large-Scale Mobility Prediction](https://mlsanity.com/paper/2510.26940/) (2025)

## Abstract (arXiv)

State-of-the-art order dispatching algorithms for ridesharing batch passenger requests and allocate them to a fleet of vehicles in a centralized manner, optimizing over the estimated values of each passenger-vehicle matching using integer linear programming (ILP). Using good estimates of future values, such ILP-based approaches are able to significantly increase the service rates (percentage of requests served) for a fixed fleet of vehicles. However, such approaches that focus solely on maximizing efficiency can lead to disparities for both drivers (e.g., income inequality) and passengers (e.g., inequality of service for different groups). Existing approaches that consider fairness only do it for naive assignment policies, require extensive training, or look at only single-sided fairness. We propose a simple incentive-based fairness scheme that can be implemented online as a part of this ILP formulation that allows us to improve fairness over a variety of fairness metrics. Deriving from a lens of variance minimization, we describe how these fairness incentives can be formulated for two distinct use cases for passenger groups and driver fairness. We show that under mild conditions, our approach can guarantee an improvement in the chosen metric for the worst-off individual. We also show empirically that our Simple Incentives approach significantly outperforms prior art, despite requiring no retraining; indeed, it often leads to a large improvement over the state-of-the-art fairness-aware approach in both overall service rate and fairness.