The concept of creating machines that can think and learn like humans—simulating processes such as learning, comprehension, problem solving, decision making, creativity, and autonomy—has existed since the early days of computing. However, artificial intelligence is not solely the result of innovative technology. It also relies on human labour for tasks like data cleaning, coding, and content classification. This type of work is often performed online on an on-demand basis, with payment provided per task through platforms such as Amazon Mechanical Turk.

In the Global South, structural dependencies often force workers to accept exploitative labour conditions. In regions with high unemployment rates, there is a constant supply of labour, and workers are often treated as disposable. Moreover, the outsourcing model enables companies to evade responsibility; when issues arise, no party takes responsibility for the workers’ well-being, leaving them vulnerable and unsupported.

In our eighth issue of the Technology, Employment, and Wellbeing blog, we would like to share the insights from the Data Inquiry Project, which is a joint project between the Weizenbaum Institute, the Technical University of Berlin, and the Distributed AI Research Lab. The project focuses on workers behind artificial intelligence and content moderation discussing their working and living environments. 

In the first articles, Milagros Miceli and Adio Dinika introduce the theoretical and practical foundations of the project.  In the second article, Camilla Salim Wagner reflects on the methodological principles guiding the project and the challenges of applying them in practice. In the third article, Krystal Kauffman continues the methodological reflections, addressing her dual role as both a data worker and a researcher. The final two articles, by Yasser Alrayes and Fasica Berhane, explore deeply personal stories of data workers caught in a cycle of instability, inadequate pay, and the pursuit of well-being in the workplace.