A Framework to Maximize Group Fairness for Workers on Online Labor Platforms

Abstract

As the number of online labor platforms and the diversity of jobs on these platforms increase, ensuring group fairness for workers needs to be the focus of job-matching services. Risk of discrimination occurs in two different job-matching services: when someone is looking for a job (i.e., a job seeker) and when someone wants to deploy jobs (i.e., a job provider). In this thesis, we propose a theoretical framework to maximize group fairness for workers 1) when job seekers are looking for jobs on multiple online labor platforms, and 2) when jobs are being deployed by job providers on multiple online labor platforms. In our proposed framework, we formulate each goal as different optimization problems with different constraints, prove most of them are computationally hard to solve and propose various efficient algorithms to solve all of them in reasonable time. We then design a series of experiments that rely on synthetic and semi-synthetic data generated from a real-world online labor platform to evaluate our proposed framework.