TY - GEN
T1 - Algorithms for fair team formation in online labour marketplaces
AU - Barnabo, Giorgio
AU - Leonardi, Stefano
AU - Fazzone, Adriano
AU - Schwiegelshohn, Chris
N1 - Funding Information:
∗The research for this work has been partially supported by the EU FET project MULTIPLEX 317532 and the ERC Advanced Grant 788893 AMDROMA "Algorithmic and Mechanism Design Research in Online Markets" This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution. WWW ’19 Companion, May 13–17, 2019, San Francisco, CA, USA © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License. ACM ISBN 978-1-4503-6675-5/19/05. https://doi.org/10.1145/3308560.3317587
Publisher Copyright:
� 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY-NC-ND 4.0 License.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/5/13
Y1 - 2019/5/13
N2 - As freelancing work keeps on growing almost everywhere due to a sharp decrease in communication costs and to the widespread of Internet-based labour marketplaces (e.g., guru.com, feelancer.com, mturk.com, upwork.com), many researchers and practitioners have started exploring the benefits of outsourcing and crowdsourcing [13, 14, 16, 23, 25, 29]. Since employers often use these platforms to find a group of workers to complete a specific task, researchers have focused their efforts on the study of team formation and matching algorithms and on the design of effective incentive schemes [2-4, 17]. Nevertheless, just recently, several concerns have been raised on possibly unfair biases introduced through the algorithms used to carry out these selection and matching procedures. For this reason, researchers have started studying the fairness of algorithms related to these online marketplaces [8, 19], looking for intelligent ways to overcome the algorithmic bias that frequently arises. Broadly speaking, the aim is to guarantee that, for example, the process of hiring workers through the use of machine learning and algorithmic data analysis tools does not discriminate, even unintentionally, on grounds of nationality or gender. In this short paper, we define the Fair Team Formation problem in the following way: given an online labour marketplace where each worker possesses one or more skills, and where all workers are divided into two or more not overlapping classes (for examples, men and women), we want to design an algorithm that is able to find a team with all the skills needed to complete a given task, and that has the same number of people from all classes. We provide inapproximability results for the Fair Team Formation problem together with four algorithms for the problem itself. We also tested the effectiveness of our algorithmic solutions by performing experiments using real data from an online labor marketplace.
AB - As freelancing work keeps on growing almost everywhere due to a sharp decrease in communication costs and to the widespread of Internet-based labour marketplaces (e.g., guru.com, feelancer.com, mturk.com, upwork.com), many researchers and practitioners have started exploring the benefits of outsourcing and crowdsourcing [13, 14, 16, 23, 25, 29]. Since employers often use these platforms to find a group of workers to complete a specific task, researchers have focused their efforts on the study of team formation and matching algorithms and on the design of effective incentive schemes [2-4, 17]. Nevertheless, just recently, several concerns have been raised on possibly unfair biases introduced through the algorithms used to carry out these selection and matching procedures. For this reason, researchers have started studying the fairness of algorithms related to these online marketplaces [8, 19], looking for intelligent ways to overcome the algorithmic bias that frequently arises. Broadly speaking, the aim is to guarantee that, for example, the process of hiring workers through the use of machine learning and algorithmic data analysis tools does not discriminate, even unintentionally, on grounds of nationality or gender. In this short paper, we define the Fair Team Formation problem in the following way: given an online labour marketplace where each worker possesses one or more skills, and where all workers are divided into two or more not overlapping classes (for examples, men and women), we want to design an algorithm that is able to find a team with all the skills needed to complete a given task, and that has the same number of people from all classes. We provide inapproximability results for the Fair Team Formation problem together with four algorithms for the problem itself. We also tested the effectiveness of our algorithmic solutions by performing experiments using real data from an online labor marketplace.
KW - Crowdsourcing
KW - Fair Set Cover
KW - Fairness
KW - Outsourcing
KW - Team Formation
UR - https://www.scopus.com/pages/publications/85066914752
U2 - 10.1145/3308560.3317587
DO - 10.1145/3308560.3317587
M3 - Conference article
AN - SCOPUS:85066914752
SP - 484
EP - 490
JO - The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019
JF - The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019
T2 - 2019 World Wide Web Conference, WWW 2019
Y2 - 13 May 2019 through 17 May 2019
ER -