Crowd work in STEM‐related fields: A window of opportunity from a gender perspective?

After the Global Financial Crisis (2008) many people found new job opportunities on crowd platforms. The COVID‐19 crisis reinforced this trend and virtual work is expected to increase. Although the working conditions of individuals engaged on these platforms is an emerging topic, of research, the existing literature tends to overlook the gendered dimension of the gig economy. Following a quantitative approach, based on the statistical analysis of 444 profiles (platform Freelancer.com in Spain and Argentina), we examine the extent to which the gig economy reproduces gender inequalities such as the underrepresentation of women in STEM‐related tasks and the gender pay gap. While the findings reveal lower participation of women than men, this gap is not higher in Argentina than in Spain. Moreover, gender variations in hourly wages are not as marked as expected, and such differences disappear once STEM skill levels are controlled for. Asymmetry in individuals' STEM skill level provides a better explanation than gender of the hourly wage differences. This finding opens a window of opportunity to mitigate the classical gender discrimination that women face in technological fields in traditional labor markets. Finally, the paper identifies some issues concerning the methodological bias entailed by the use of an application programming interface in cyber‐research, when analyzing gender inequalities.

Crowd work in STEM‐related fields: A window of opportunity from a gender perspective?

After the Global Financial Crisis (2008) many people found new job opportunities on crowd platforms. The COVID-19 crisis reinforced this trend and virtual work is expected to increase. Although the working conditions of individuals engaged on these platforms is an emerging topic, of research, the existing literature tends to overlook the gendered dimension of the gig economy. Following a quantitative approach, based on the statistical analysis of 444 profiles (platform Freelancer.com in Spain and Argentina), we examine the extent to which the gig economy reproduces gender inequalities such as the underrepresentation of women in STEM-related tasks and the gender pay gap. While the findings reveal lower participation of women than men, this gap is not higher in Argentina than in Spain. Moreover, gender variations in hourly wages are not as marked as expected, and such differences disappear once STEM skill levels are controlled for. Asymmetry in individuals' STEM skill level provides a better explanation than gender of the hourly wage differences. This finding opens a window of opportunity to mitigate the classical gender discrimination that women face in technological fields in traditional labor markets. Finally, the paper identifies some issues concerning the methodological bias entailed by the use of an application programming interface in cyber-research, when analyzing gender inequalities.

What Quality Control Mechanisms do We Need for High-Quality Crowd Work?

Crowd sourcing and human computation has slowly become a mainstay for many application areas that seek to leverage the crowd in the development of high quality datasets, annotations, and problem solving beyond the reach of current AI solutions. One of the major challenges to the domain is ensuring high-quality and diligent work. In response, the literature has seen a large number of quality control mechanisms each voicing (sometimes domain-specific) benefits and advantages when deployed in largescale human computation projects. This creates a complex design space for practitioners: it is not always clear which mechanism(s) to use for maximal quality control. In this article, we argue that this decision is perhaps overinflated and that provided there is "some kind" of quality control that this obviously known to crowd workers this is sufficient for "high-quality" solutions. To evidence this, and provide a basis for discussion, we undertake two experiments where we explore the relationship between task design, task complexity, quality control and solution quality. We do this with tasks from natural language processing, and image recognition of varying complexity. We illustrate that minimal quality control is enough to repel constantly underperforming contributors and that this is constant across tasks of varying complexity and formats. Our key takeaway: quality control is necessary, but seemingly not how it is implemented.