Fair Sharing for Sharing Economy Platforms

Abstract

Sharing economy platforms, such as Airbnb, Uber or eBay, are an increasingly common way for people to provide their services to earn a living. Yet, the focus in these platforms is either on the satisfaction of the customers of the service, or on boosting successful business transactions. However, recent studies provide a multitude of reasons to worry about the providers in the sharing economy ecosystems. The concerns range from bad working conditions and worker manipulation to discrimination against minorities. This is worsened by the fact that the algorithms used for matching customers and providers, that de facto decide the amount of exposure each provider receives, are proprietary and non-transparent. In this position paper, we propose a novel framework to think about fairness in the matching mechanisms of online sharing economy platforms. Specifically, we focus on various fairness guarantees from the providers' perspective. Our notion of fairness relies on the idea that, spread over time, all providers should receive the amount of exposure proportional to their relevance or the utility they provide. We postulate that by not requiring every match to be fair, but rather distributing the fairness over time, we can (i) give better guarantees in terms of the overall benefit for the providers and the customers, (ii) make use of implementations from a long line of research concerned with fair division of constrained resources. Overall, our work takes the first step towards rethinking fairness in online sharing economy systems with an additional emphasis on the well-being of providers, and provides insights into parallels with well-established practical implementations in other domains.

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

“…FairRec causes only up to 0.2 fraction or 20% loss in exposure in comparison to top-k owing to the intelligent selection approach of FairRec. It is worth noticing that MMS for LF is low (MMS= 0 for k < 10, MMS= 1 for k ∈ [10,18], MMS= 2 for k ∈ [20,29],...). MMS is satisfied for all producers until k = 9; but at k=10, MMS is not guaranteed for all producers, and thus, we see a drop in performance at k = 10 which happens again at k = 19.…”

Understanding Inequalities in Ride-Hailing Services Through Simulations

“…fixing the algorithm. Lastly, we examine whether we can incorporate the fairness perspective into our system and achieve more equal incomes while conserving the overall revenue, similarly to the perspective of 33 . Our goal is to keep track of drivers' income throughout the day and take it into account when assigning rides.…”

“…Such approaches aim to maximize the benefits for the company or to minimize the adverse effects such as CO 2 emissions, overall distances driven, or the passenger waiting times. Following the line of fairness measurement literature [32][33][34][35] , we instead focus on the fair distribution of income from the drivers perspective, because current systems do not guarantee the same income for the same amount of work, neither across workers nor over time 9,10,19,21 .We use an agent-based simulation to systematically study the mechanisms in ride-hailing and delivery systems from the perspective of the drivers for a fixed timeframe of one week. Given the social context of the problem, the timeframe of interest is defined to be relatively short: workers of ride-hailing companies have to focus on daily and weekly income targets because of regular fees and payments 15,36,37 .…”

“…Such approaches aim to maximize the benefits for the company or to minimize the adverse effects such as CO 2 emissions, overall distances driven, or the passenger waiting times. Following the line of fairness measurement literature [32][33][34][35] , we instead focus on the fair distribution of income from the drivers perspective, because current systems do not guarantee the same income for the same amount of work, neither across workers nor over time 9,10,19,21 .…”

Towards Systematic Specification of Non-Functional Requirements for Sharing Economy Systems

“…Hence, in addition to the inherent benefits of utilizing ICT, demand side economies of scale (more customers attracting more providers and vice versa) can boost transaction density, revenues and the value of the platform. In order to operate such an SE service efficiently, incentives for all three stakeholder types should align well [21]. In such environments, there are two key issues that are universal in distributed systems in general, specifically in the SE sector: fairness and efficiency.…”