Artificial intelligence depends on human labour to conduct tasks such as data cleaning, coding, and classifying content. This on-demand work is offered and performed online, paid by the task, on platforms like Amazon Mechanical Turk. Conceptualized as ‘ghost work’, this rapidly growing, platform-based work is largely unseen: workers are unable to speak with managers, do not get feedback, and lack labour protections. How do these specific work conditions influence ghost workers’ well-being? To ensure decent work conditions as automation continues to expand, knowledge about the effects of ghost work on well-being is urgently needed.
The proposed project will develop and test an integrative framework for analysing the effects of ghost work on worker’s well-being. Existing models for analysing the impact of work conditions on well-being fall short for studying ghost work, as these models assume a person has a job and most likely an employer and colleagues. Therefore, this project begins from the specificities of ghost work to synthesize theories and concepts about algorithmic control, occupational well-being, human computation, and platform labour, in order to understand how and through which mechanisms ghost work influences well-being.
The project will contribute to and advance cross-disciplinary scholarship on platform labour and organizational studies of algorithmic technologies. Using a multi-methodological approach to study the effects of ghost work, it begins with in-depth interview-based fieldwork on ghostworkers’ work conditions, and then entails qualitative diary studies of the short-term dynamics of ghost work for worker’s work conditions and well-being. Finally, a 4-wave longitudinal panel study will investigate the relationship between ghost work and well-being over time. Scholars in multiple fields, as well as policy makers and industry leaders, will be keenly interested in both the resulting integrative framework and empirical findings.
Fields of science
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