Summary of Long-term Fairness in Ride-hailing Platform, by Yufan Kang et al.
Long-term Fairness in Ride-Hailing Platform
by Yufan Kang, Jeffrey Chan, Wei Shao, Flora D. Salim, Christopher Leckie
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to address fairness issues in ride-hailing, which has been largely overlooked despite the recent surge of research focusing on optimizing efficiency. The current studies exploiting traditional optimization methods and Markov Decision Process (MDP) methods have limitations, including myopic short-term decision-making and instability of fairness over a longer horizon. To overcome these challenges, the proposed dynamic MDP model incorporates two key features: a prediction module that forecasts future requests from different locations to consider long-term fairness, and a customized scalarization function for multi-objective multi-agent Q Learning that balances efficiency and fairness. Experimental results on a real-world dataset demonstrate the effectiveness of this approach in outperforming existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ride-hailing companies like Uber and Lyft are super popular, but did you know there’s an unfair problem? Some drivers earn way more than others, and passengers in certain areas have to wait longer for a ride. This is not good for the economy or people’s feelings! Scientists tried to fix this by using old ways of solving problems, but those methods had some big flaws. They didn’t think about what would happen in the future or how fairness would change over time. To solve these problems, researchers created a new way to balance efficiency and fairness. This method uses two cool tools: one that predicts what will happen in the future and another that makes sure everything is fair and good for everyone. |
Keywords
* Artificial intelligence * Optimization
