Fairness and Transparency in Crowdsourcing

Ria Mae Borromeo (1) , Thomas Laurent (1) , Motomichi Toyama (1) , Sihem Amer-Yahia (2)

1 KEIO UNIVERSITY - Department of Mathematics
2 SLIDE - ScaLable Information Discovery and Exploitation [Grenoble]

Despite the success of crowdsourcing, the question of ethics has not yet been addressed in its entirety. Existing efforts have studied fairness in worker compensation and in helping requesters detect malevolent workers. In this paper, we propose fairness axioms that generalize existing work and pave the way to studying fairness for task assignment, task completion, and worker compensation. Transparency on the other hand, has been addressed with the development of plug-ins and forums to track workers' performance and rate requesters. Similarly to fairness, we define transparency axioms and advocate the need to address it in a holistic manner by providing declarative specifications. We also discuss how fairness and transparency could be enforced and evaluated in a crowdsourcing platform.

Keywords: Crowdsourcing; Fairness; Declarative Transparency

Domains: Data Structures and Algorithms [cs.DS]

Ria Mae Borromeo, Thomas Laurent, Motomichi Toyama, Sihem Amer-Yahia. Fairness and Transparency in Crowdsourcing. International Conference on Extending Database Technology (EDBT), Mar 2017, Venice, Italy. ⟨10.5441/002/edbt.2017.46⟩. ⟨hal-02001900⟩