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

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 against workers occurs in two different job-matching services: when someone is looking for a job (...

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Bibliographic Details
Published in:Data science and engineering 2023-06, Vol.8 (2), p.146-176
Main Authors: Rabaa, Anis El, Elbassuoni, Shady, Hanna, Jihad, Mouawad, Amer E., Olleik, Ayham, Amer-Yahia, Sihem
Format: Article
Language:English
Subjects:
Algorithm Analysis and Problem Complexity
Algorithms
Artificial Intelligence
Chemistry and Earth Sciences
Computer engineering
Computer Science
Crowdsourcing
Data Mining and Knowledge Discovery
Database Management
Employment
Group fairness
Job hunting
Job provider
Job seeker
Labor
Matching
Online labor platforms
Optimization
Physics
Platforms
Research Paper
Statistics for Engineering
Synthetic data
Systems and Data Security
Workers
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Summary: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 against workers 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). To maximize their chances of getting hired, job seekers submit their profiles on different platforms. Similarly, job providers publish their job offers on multiple platforms with the goal of reaching a wide and diverse workforce. In this paper, we propose a theoretical framework to maximize group fairness for workers 1) when job seekers are looking for jobs on multiple platforms, and 2) when jobs are being deployed by job providers on multiple platforms. 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 framework.
ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-023-00213-y