Unleashing the Potential of Crowd Work: The Need for a Post-Taylorism Crowdsourcing Model

Paid crowdsourcing connects task requesters to a globalized, skilled workforce that is available 24/7. In doing so, this new labor model promises not only to complete work faster and more efficiently than any previous approach but also to harness the best of our collective capacities. Nevertheless, for almost a decade now, crowdsourcing has been limited to addressing rather straightforward and simple tasks. Large-scale innovation, creativity, and wicked problem-solving are still largely out of the crowd’s reach. In this opinion paper, we argue that existing crowdsourcing practices bear significant resemblance to the management paradigm of Taylorism. Although criticized and often abandoned by modern organizations, Taylorism principles are prevalent in many crowdsourcing platforms, which employ practices such as the forceful decomposition of all tasks regardless of their knowledge nature and the disallowing of worker interactions, which diminish worker motivation and performance. We argue that a shift toward post-Taylorism is necessary to enable the crowd address at scale the complex problems that form the backbone of today’s knowledge economy. Drawing from recent literature, we highlight four design rules that can help make this shift, namely, endorsing social crowd networks, encouraging teamwork, scaffolding ownership of one’s work within the crowd, and leveraging algorithm-guided worker self-coordination.

Lean Privacy Review: Collecting Users’ Privacy Concerns of Data Practices at a Low Cost

Today, industry practitioners (e.g., data scientists, developers, product managers) rely on formal privacy reviews (a combination of user interviews, privacy risk assessments, etc.) in identifying potential customer acceptance issues with their organization’s data practices. However, this process is slow and expensive, and practitioners often have to make ad-hoc privacy-related decisions with little actual feedback from users. We introduce Lean Privacy Review (LPR), a fast, cheap, and easy-to-access method to help practitioners collect direct feedback from users through the proxy of crowd workers in the early stages of design. LPR takes a proposed data practice, quickly breaks it down into smaller parts, generates a set of questionnaire surveys, solicits users’ opinions, and summarizes those opinions in a compact form for practitioners to use. By doing so, LPR can help uncover the range and magnitude of different privacy concerns actual people have at a small fraction of the cost and wait-time for a formal review. We evaluated LPR using 12 real-world data practices with 240 crowd users and 24 data practitioners. Our results show that (1) the discovery of privacy concerns saturates as the number of evaluators exceeds 14 participants, which takes around 5.5 hours to complete (i.e., latency) and costs 3.7 hours of total crowd work ( $80 in our experiments); and (2) LPR finds 89% of privacy concerns identified by data practitioners as well as 139% additional privacy concerns that practitioners are not aware of, at a 6% estimated false alarm rate.

Online Platforms and "Depending Work" After Uber

Digitalization is bringing new challenges, including the way how people used to work. The future of work is uncertain. Technology driven innovations are changing the way, how society react to such development by creating different types of jobs and workplaces. What is important today can be redundant tomorrow. Having said that the labour law and civil law will most probably need to react in certain way. The main aim of this paper is to focus on the specific types of activities – such as virtual work or crowd work as well as on relationships between digital platforms, workers, employers and clients while offering and providing services via online platforms. Further the paper will outline the responsibility of online platforms if considered to be in a position of an employer. Due to the lack of compliance with labour laws related duties the online platforms are gaining the unfair competition advantage comparing with "traditional" employer. When it comes to the virtual workers, they can potentially suffer from inadequate or limited access to the certain kind of protection (when compared to the "traditional" employees).

Crowd Work und Plattformökonomie

This paper addresses the question of whether crowd workers on microtask platforms should be classified as employees. For years, this has been disputed from the perspective of labor law, but with a certain tendency to deny it. However, the BAG ruled in December 2020 that a crowd worker can be an employee by and large. So how are the circumstances to be assessed that make a crowd worker an employee (or non-employee)? That is what this paper investigates. In the first part of the paper, we review various empirical studies on crowd work and analyze the functioning of platforms. Economic and organizational sociology are also considered. In the second part, concrete case analyses from a self-experiment are presented in order to be able to evaluate them in terms of labor law. In the third part, two aspects are opened up, firstly, how the divergence between the national and the European legal concept of employee is again revealed in crowd work. On the other hand, new forms of regulation are suggested: For example, does Regulation P2B Regulation (2019/1150) not fit many of the problems of platform work? What about its applicability to platform work? What about "regulation by design"? Finally, it must be considered, how labor law can cope with new forms of work and, above all, forms of new organizational methods.