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

• URL: http://arxiv.org/abs/2303.14332v1
• Date: Sat, 25 Mar 2023 02:24:27 GMT
• Title: Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems
• Authors: Ashwin Kumar, Yevgeniy Vorobeychik, William Yeoh
• Abstract summary: We propose a simple incentive-based fairness scheme that can be implemented online as a part of this ILP formulation. We show how these fairness incentives can be formulated for two distinct use cases for passenger groups and driver fairness.
• Score: 27.34946988130242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation [3.959853359438669]
  We introduce FairLoRA, a novel fairness-specific regularizer for Low Rank Adaptation (LoRA) Our results demonstrate that the need for higher ranks to mitigate bias is not universal; it depends on factors such as the pre-trained model, dataset, and task.
  arXiv  Detail & Related papers  (2024-10-22T18:50:36Z)
• Cooperation and Fairness in Multi-Agent Reinforcement Learning [6.164771707307928]
  In resource-constrained environments of mobility and transportation systems, efficiency may be achieved at the expense of fairness. We consider the problem of fair multi-agent navigation for a group of decentralized agents using multi-agent reinforcement learning (MARL) We find that our model yields a 14% improvement in efficiency and a 5% improvement in fairness over a baseline trained using random assignments.
  arXiv  Detail & Related papers  (2024-10-19T00:10:52Z)
• Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem [21.148097893483406]
  DVRPs involve two stakeholders: service providers that deliver services to customers and customers who raise requests from different locations. We propose a novel framework, a Two-sided Fairness-aware Genetic Algorithm (named 2FairGA), which expands the genetic algorithm from the original objective solely focusing on utility to multi-objectives that incorporate two-sided fairness.
  arXiv  Detail & Related papers  (2024-05-29T15:24:28Z)
• Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System [7.531863938542706]
  The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method.
  arXiv  Detail & Related papers  (2023-12-29T23:02:34Z)
• Fair-CDA: Continuous and Directional Augmentation for Group Fairness [48.84385689186208]
  We propose a fine-grained data augmentation strategy for imposing fairness constraints. We show that group fairness can be achieved by regularizing the models on transition paths of sensitive features between groups. Our proposed method does not assume any data generative model and ensures good generalization for both accuracy and fairness.
  arXiv  Detail & Related papers  (2023-04-01T11:23:00Z)
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• Improving Robust Fairness via Balance Adversarial Training [51.67643171193376]
  Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes. We propose Adversarial Training (BAT) to address the robust fairness problem.
  arXiv  Detail & Related papers  (2022-09-15T14:44:48Z)
• How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts [107.72786199113183]
  We propose a novel fairness learning method termed CUrvature MAtching (CUMA) CUMA achieves robust fairness generalizable to unseen domains with unknown distributional shifts. We evaluate our method on three popular fairness datasets.
  arXiv  Detail & Related papers  (2022-07-04T02:37:50Z)
• Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling [10.613763106603272]
  We investigate two methods to reduce forms of inequality in ride-pooling platforms. First, we find that optimizing for driver-side fairness outperforms state-of-the-art models on the number of riders serviced. Second, we explore income redistribution as a way to combat income inequality by having drivers keep an $r$ fraction of their income, and contribute the rest to a redistribution pool.
  arXiv  Detail & Related papers  (2021-10-07T14:53:37Z)
• Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing [84.47891614815325]
  This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We introduce novel notions of fairness and stability in P2P ridesharing. Results suggest that fair and stable solutions can be obtained in reasonable computational times.
  arXiv  Detail & Related papers  (2021-10-04T02:14:49Z)
• SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness [50.916483212900275]
  We first formulate a version of individual fairness that enforces invariance on certain sensitive sets. We then design a transport-based regularizer that enforces this version of individual fairness and develop an algorithm to minimize the regularizer efficiently.
  arXiv  Detail & Related papers  (2020-06-25T04:31:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.