2019, WWW, Algorithms for Fair Team Formation in Online Labour

[Rounique]

Marketplaces∗

[Rounique]

Title: Algorithms for Fair Team Formation in Online Labour Marketplaces∗
Venue: WWW
Year: 2019

Introdcution:
Researchers have concentrated their efforts on the study of team formation and matching algorithms since companies frequently utilize these platforms to recruit a group of workers to perform a certain assignment.

Fair Team Formation Definition:

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 example, 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.



Definition of “unfair” in this work:

Treating someone differently based on her/his group membership, and not their merit.



Type of the bias:

The algorithmic bias, which occurs when the bias is added by the algorithm itself or by the way this algorithm manages the bias present in the data it crunches, is one of the many different sources of bias on the Web that in this paper has been working on, as a solution to the Fair Team Formation problem.



Formalizing the Fair Team Formation problem:
The 
problem of finding the cheapest team that can complete the task and, at the same time, that counts the same number of people from two not overlapping classes.

Algorithms:
This work presents 4 algorithms: The first three are partially based on greedy Set Cover Problem and the 4th one is based on linear programming formulation of Fair Team Formation.

Future work:
This work is limited to no more than 6 skills, therefore working on this could be an extension of the work