The Ever Evolving Online Labor Market: Overview, Challenges and Opportunities

The goal of this tutorial is to make the audience aware of various discipline-specific research activities that could be characterized to be part of online labor markets and advocate for a unified framework that is interdisciplinary in nature and requires convergence of different research disciplines. We will discuss how such a framework could bring transformative effect on the nexus of humans, technology, and the future of work. PVLDB Reference Format: Sihem Amer-Yahia, Senjuti Basu Roy. The Ever Evolving Online Labor Market: Overview, Challenges and Opportunities. PVLDB, 12(12): 1978-1981, 2019. DOI: https://doi.org/10.14778/3352063.3352114 1. OVERVIEW AND RELEVANCE The rapid development of professional social networks and online labor markets, is affecting the future of jobs and workers. Professional social networks such as LinkedIn, are revolutionizing hiring practices. An increasing number of individuals rely on such networks to find jobs, and it is becoming common practice for head hunters and companies to examine one’s profile on LinkedIn before contacting or hiring someone. Online job marketplaces are gaining popularity as mediums to hire people to perform certain tasks. These marketplaces include freelancing platforms such as Qapa and MisterTemp’ in France, and TaskRabbit and Fiverr in the USA. On those platforms, workers can find temporary jobs in the physical world (e.g., looking for a plumber), or in the form of virtual “micro-gigs” such as “help with HTML, JavaScript, CSS, and JQuery”. Crowdsourcing platforms are a very popular type of online job marketplaces nowadays. These platforms are fully virtual: workers are hired online and tasks are also completed online. Examples of crowdsourcing platforms are FouleFactory, and Prolific Academic in Europe, and Amazon Mechanical Turk and Figure Eight in the USA. As the gig economy grows, an important offshoot of this movement is “flash organizations”, the This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment. Proceedings of the VLDB Endowment, Vol. 12, No. 12 ISSN 2150-8097. DOI: https://doi.org/10.14778/3352063.3352114 pop-up shops that come together for a purpose, such as developing and marketing an app. They include entire teams that work closely together, and when the project is complete, the teams disband. For example, Gigster.com is a smart development service that brings freelancers together into software-building teams on demand at any time, or Artella.com is designed for creating complex animated features, offering teams of freelance animators, sound designers, and other talent at your beck and call. From a conceptual standpoint, online labor markets can be characterized as follows: workers are the producers of work that are to be consumed by systems, platforms, or organizations. Workers need to be hired, treated, and compensated fairly, they have to be assisted to accomplish their work or further their career. On the other hand, consumers, such as, business, organizations, or platforms need to hire an appropriate workforce to be able to accomplish their business goal in time, with certain accuracy, and within budget. Social science as well as data-centric research [5, 7, 8, 10, 13, 14, 18, 23, 26, 27] have developed disconnected and discipline-specific approaches to solve different problems in online labor markets. For example, social science researchers have proposed conceptual frameworks, visualization, and software prototypes to recruit workers and form appropriate teams, decompose complex tasks to assist workers, or propose tools to enable interactivity and collaboration among workers. Database research, on the other hand, has addressed scalability and data management challenges of how to curate and clean data produced by online labor markets, how to model the data and store it effectively, or how to form appropriate teams [3, 21, 22]. Machine learning research [7, 14, 27] proposed models that are capable of aggregating workers’ contributions or infer/estimate groundtruths when that is unknown. Finally, psychology and organizational research [17, 25] proposed conceptual models that are required to understand and analyze human behavior in online labor market. We will first describe different applications that rely on free-lancing and online labor markets. Then, we will re-visit seminal and prominent works that came out from different research communities (social science, machine learning, data management, psychology and organizational research, and economics) on the topic of online labor markets, describe their approaches and summarize their impact on science, society and industry. Finally, we will outline the requirements of a unified framework that has the potential to combine the best of all these worlds and present modeling, data management, and algorithmic challenges to conceptualize it.