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.