Trade the System Efficiency for the Income Equality of Drivers in Rideshare

Abstract

<jats:p>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.</jats:p>

Fairness Maximization among Offline Agents in Online-Matching Markets

“…Nanda et al [36] proposed a bi-objective online-matching-based model to study the tradeoff between the system efficiency (profit) and the fairness among rideshare riders during high-demand hours. In contrast, Xu and Xu [37] presented a similar model to examine the tradeoff between the system efficiency and the income equality among rideshare drivers. Unlike focusing on one single objective of fairness maximization like here, both studies in [36] and [37] seek to balance the objective of fairness maximization with that of profit maximization.…”

“…In contrast, Xu and Xu [37] presented a similar model to examine the tradeoff between the system efficiency and the income equality among rideshare drivers. Unlike focusing on one single objective of fairness maximization like here, both studies in [36] and [37] seek to balance the objective of fairness maximization with that of profit maximization. Recently, Ma et al [38] considered a similar problem to us but focus on the fairness among online agents.…”

“…We test our algorithms of IFM and GFM on a public ridehailing dataset, which is collected from the city of Chicago 10 . Following the setting in [36,37], we focus on a short time window and assume that drivers are offline agents while riders are online agents that arrive dynamically. Our goal is to maximize individual and group fairness among all drivers.…”

Equity Promotion in Online Resource Allocation

“…Justification of parameters {𝜇 𝑔 }. Observe that previous studies (Ma, Xu, and Xu 2020;Nanda et al 2020;Xu and Xu 2020) have chosen a fairness metric as min 𝑔 ∈ G E[𝑋 𝑔 ]/E[ 𝐴 𝑔 ], the minimum ratio of the expected number of agents served to that of arrivals among all groups. This can be cast as a special case of ASR by setting each…”

“…One technical challenge in addressing (internal) equity promotion in online resource allocation is how to craft a proper metric of equity. Current metrics of equity proposed so far are all based on the minimum ratio of the number of requesters served to that of total arrivals within any given group (Ma, Xu, and Xu 2020;Nanda et al 2020;Xu and Xu 2020). Unfortunately, these metrics fail to capture the exact equity we expect in practice.…”