Towards Fairness Issues in Spatial Crowdsourcing

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Towards Fairness Issues in Spatial Crowdsourcing"

By

Mr. Zhao CHEN


Abstract

With the booming mobile Internet and sharing economy, online spatial 
crowdsourcing services, such as Uber and Didi, are becoming important 
infrastructures of our daily life. Existing studies about spatial 
crowdsourcing usually focus on the platform interests and ignore experiences 
of individual requesters and workers. Because of the dynamically changed 
task demand and worker supply, the major experience issue, for both workers 
and requesters, is caused by insufficient valid assignments. In this thesis 
we discuss the problem of how to allocate limited assignment resources 
fairly to both worker and requester sides.

For a requester, because his/her task needs to be assigned as soon as 
possible, the resource is nearby available workers. Thus, we study the 
minimizing maximum delay spatial crowdsourcing (MMD-SC) problem and propose 
solutions aiming at achieving a worst case controlled task assignment. The 
MMD-SC problem assumes that both workers and requesters come dynamically and 
considers not only the workers' traveling time costs but also the buffering 
time of tasks, thus it is very challenging due to two-sided online setting. 
To address these challenges, we propose a space embedding based online 
random algorithm and two efficient heuristic algorithms.

For the worker side, the resource is new tasks which need to be distributed 
to workers as equally as possible. The first challenge is to formally define 
the worker fairness and combine it with existing platform level goals, and 
the second challenge is to conduct task assignment with consideration of 
worker fairness and platform interests. To address these challenges, we 
formally define an online bi-objective matching problem, namely the 
worker-fairness-aware assignment problem (WFAA), and some special 
cases/variants of it to fit in most spatial crowdsourcing scenarios. We give 
corresponding solutions for different cases of WFAA. Particularly, we show 
that the dynamic sequential case, which is a generalization of an existing 
fairness scheduling problem, can be solved with an O(n) fairness cost bound 
(n is the total worker number), and give an O(n/m) fairness cost bound for 
the m-sized general batch case (m is the minimum batch size).

In addition, we show the effectiveness and efficiency of our methods via 
extensive experiments on both synthetic and real datasets.


Date:			Friday, 28 August 2020

Time:			2:00pm - 4:00pm

Zoom Meeting:		https://hkust.zoom.us/j/97935850729

Chairperson:		Prof. Li QIU (ECE)

Committee Members:	Prof. Lei CHEN (Supervisor)
 			Prof. Qiong LUO
 			Prof. Ke YI
 			Prof. Can YANG (MATH)
 			Prof. Guoliang LI (Tsinghua University)


**** ALL are Welcome ****