The Duality of Empowerment and Marginalization in Microtask Crowdsourcing: A Replication

This paper describes an exact replication of a study by Deng, Joshi, & Galliers (2016) of crowd worker values on Amazon’s Mechanical Turk (MTurk) crowdsourcing platform. The original study analyzed 210 MTurk crowd workers’ narratives using value sensitive design (VSD). The results uncovered nine shared values: access, autonomy, fairness, transparency, communication, security, accountability, making an impact, and dignity. Further analysis in the original study revealed four crowdsourcing structures: compensation, governance, technology, and microtask, and duality of crowd worker perceptions, empowerment, and marginalization. This replication study also asked Amazon Mechanical Turk crowd workers questions about their work and used the original study’s findings for a priori codes. However, new values and findings emerged in our results, which offers additional implications for further research regarding microtask crowdsourcing

It's getting crowded! : improving the effectiveness of microtask crowdsourcing

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Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation

A human computation system can be viewed as a distributed system in which the processors are humans, called workers. Such systems harness the cognitive power of a group of workers connected to the Internet to execute relatively simple tasks, whose solutions, once grouped, solve a problem that systems equipped with only machines could not solve satisfactorily. Examples of such systems are Amazon Mechanical Turk and the Zooniverse platform. A human computation application comprises a group of tasks, each of them can be performed by one worker. Tasks might have dependencies among each other. In this study, we propose a theoretical framework to analyze such type of application from a distributed systems point of view. Our framework is established on three dimensions that represent different perspectives in which human computation applications can be approached: quality-of-service requirements, design and management strategies, and human aspects. By using this framework, we review human computation in the perspective of programmers seeking to improve the design of human computation applications and managers seeking to increase the effectiveness of human computation infrastructures in running such applications. In doing so, besides integrating and organizing what has been done in this direction, we also put into perspective the fact that the human aspects of the workers in such systems introduce new challenges in terms of, for example, task assignment, dependency management, and fault prevention and tolerance. We discuss how they are related to distributed systems and other areas of knowledge.Comment: 3 figures, 1 tabl

Hourly Wages in Crowdworking: A Meta-Analysis

In the past decade, crowdworking on online labor market platforms has become an important source of income for a growing number of people worldwide. This development has led to increasing political and scholarly interest in the wages people can earn on such platforms. This study extends the literature, which is often based on a single platform, region, or category of crowdworking, through a meta-analysis of prevalent hourly wages. After a systematic literature search, the paper considers 22 primary empirical studies, including 105 wages and 76,765 data points from 22 platforms, eight different countries, and 10 years. It is found that, on average, microtasks results in an hourly wage of less than $6. This wage is significantly lower than the mean wage of online freelancers, which is roughly three times higher when not factoring in unpaid work. Hourly wages accounting for unpaid work, such as searching for tasks and communicating with requesters, tend to be significantly lower than wages not considering unpaid work. Legislators and researchers evaluating wages in crowdworking need to be aware of this bias when assessing hourly wages, given that the majority of literature does not account for the effect of unpaid work time on crowdworking wages. To foster the comparability of different research results, the article suggests that scholars consider a wage correction factor to account for unpaid work. Finally, researchers should be aware that remuneration and work processes on crowdworking platforms can systematically affect the data collection method and inclusion of unpaid work

An Examination of the Work Practices of Crowdfarms

Crowdsourcing is a new value creation business model. Annual revenue of the Chinese market alone is hundreds of millions of dollars, yet few studies have focused on the practices of the Chinese crowdsourcing workforce, and those that do mainly focus on solo crowdworkers. We have extended our study of solo crowdworker practices to include crowdfarms, a relatively new entry to the gig economy: small companies that carry out crowdwork as a key part of their business. We report here on interviews of people who work in 53 crowdfarms. We describe how crowdfarms procure jobs, carry out macrotasks and microtasks, manage their reputation, and employ different management practices to motivate crowdworkers and customers

The Glamorisation of Unpaid Labour: AI and its Influencers

To harness the true potential of Artificial Intelligence (AI) for sustainability and societal betterment, we need to move away from the goals of racing to mimic human behaviour and prioritising corporate interests, where workers are continuously exploited. The unpaid labour and societal harms which are generated from Digital Value Networks (DVNs) used by companies producing AI needs to be regulated. Unethical data collection and data labelling practices have serious consequences, as evidenced by the case studies reviewed in this short paper, such as with influencer marketing. This paper addresses important neglected areas of study in worker and user data and labeling exploitation practices, where ethical AI could be impactful.Comment: 4 pages, 2 pages of references, Deep Learning Indaba 2023 Short Pape

Microwork: Theory, Models and Mechanics for enabling impact through aggregate action

This major research project will focus on the primary investigation area of microwork. Several sub-areas of inquiry will be visited in order to explore potential new directions, determine and suggest factors potentially maximizing impact via microwork projects, including historical examples of analog micro-tasks and their possible correlations to both existing and future digital microwork; the mechanized design elements for executing microwork projects, including drivers, challenges and opportunities, and ultimately the potential for future impacts via microwork, on individual and collective levels, with focus on increasing social impact, and volumes of action. In turn, this combined understanding will suggest the formation of a new microwork model, as well as a business model canvas for evaluation, by helping to suggest the theoretical and physical components required for success, such as new socially-based drivers, tools, mechanics, success metrics, and processes