The unwitting labourer: extracting humanness in AI training

Abstract

Many modern digital products use Machine Learning (ML) to emulate human abilities, knowledge, and intellect. In order to achieve this goal, ML systems need the greatest possible quantity of training data to allow the Artificial Intelligence (AI) model to develop an understanding of “what it means to be human”. We propose that the processes by which companies collect this data are problematic, because they entail extractive practices that resemble labour exploitation. The article presents four case studies in which unwitting individuals contribute their humanness to develop AI training sets. By employing a post-Marxian framework, we then analyse the characteristic of these individuals and describe the elements of the capture-machine. Then, by describing and characterising the types of applications that are problematic, we set a foundation for defining and justifying interventions to address this form of labour exploitation.

Africa, ChatGPT, and Generative AI Systems: Ethical Benefits, Concerns, and the Need for Governance

“…There are possibilities of replacement jobs related to operations that can be performed with these models, and as the integration of this technology into business operations continues to increase, employees may be expected to upskill or reskill to adapt to the ecosystem. Most critically, there have been reports of labour exploitation in Africa as Open AI designed its model [53,54]. However, these models could help to create new jobs, assist employees via the automation of difficult tasks, and empower people to launch their own businesses.…”