On the task assignment with group fairness for spatial crowdsourcing |
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| Institution: | 1. School of Business and Management, Jilin University, Changchun, China;3. School of Management, Harbin Institute of Technology, Harbin, China;1. Faculty of Social and Political Sciences, Institute of Social Sciences, Life Course and Social Inequality Research Centre, Lausanne University, Switzerland;2. Institute of Computational Linguistics, Zurich University, Switzerland;1. Department of Management Science and Engineering, School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China;2. School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China;1. Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and MNR Technology Innovation Center of Territorial and Spatial Big Data and Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China;2. Logistics Information Centre, Beijing 100842, China;3. Department of Game Design, Faculty of Arts, Uppsala University, Sweden |
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| Abstract: | Task assignment, the core problem of Spatial Crowdsourcing (SC), is often modeled as an optimization problem with multiple constraints, and the quality and efficiency of its solution determines how well the SC system works. Fairness is a critical aspect of task assignment that affects worker participation and satisfaction. Although the existing studies on SC have noticed the fairness problem, they mainly focus on fairness at the individual level rather than at the group level. However, differences among groups in certain attributes (e.g. race, gender, age) can easily lead to discrimination in task assignment, which triggers ethical issues and even deteriorates the quality of the SC system. Therefore, we study the problem of task assignment with group fairness for SC. According to the principle of fair budget allocation, we define a well-designed constraint that can be considered in the task assignment problem of SC systems, resulting in assignment with group fairness. We mainly consider the task assignment problem in a common One-to-One SC system (O2-SC), and our goal is to maximize the quality of the task assignment while satisfying group fairness and other constraints such as budget and spatial constraints. Specifically, we first give the formal definition of task assignment with group fairness constraint for O2-SC. Then, we prove that it is essentially an NP-hard combinatorial optimization problem. Next, we provide a novel fast algorithm with theoretical guarantees to solve it. Finally, we conduct extensive experiments using both synthetic and real datasets. The experimental results show that the proposed constraint can significantly improve the group fairness level of algorithms, even for a completely random algorithm. The results also show that our algorithm can efficiently and effectively complete the task assignment of SC systems while ensuring group fairness. |
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| Keywords: | Spatial crowdsourcing Group fairness Human-centered algorithm Optimization algorithm |
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