{"type":"ScholarlyArticle","title":"Trade the System Efficiency for the Income Equality of Drivers in Rideshare","authors":[{"name":"Yifan Xu","slug":"yifan-xu","given":"Yifan","family":"Xu","affiliation":"Key Lab of CNII , MOE , Southeast University , Nanjing , China"},{"name":"Pan Xu","slug":"pan-xu","given":"Pan","family":"Xu","affiliation":"Department of Computer Science , New Jersey Institute of Technology , Newark , USA"}],"abstract":"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.","doi":"10.24963/ijcai.2020/580","url":"https://ijcai.org/proceedings/2020/580","pdf":"https://ijcai.org/proceedings/2020/0580.pdf","markdown":"https://ijcai.org/proceedings/2020/580.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/580","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Planning and Scheduling","track":"Main track","number":580,"pages":{"first":4199,"last":4205},"keywords":["Agent-based and Multi-agent Systems: Resource Allocation","Planning and Scheduling: Scheduling","AI Ethics: Fairness"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/26401/file_26401.m3u8","length_sec":671,"provider":"hls"}],"references":[{"position":1,"raw":"[Aggarwal et al., 2014] Gagan Aggarwal, Yang Cai, Aranyak Mehta, and George Pierrakos. 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