The training and coding of AI systems, particularly generative ones, depend on the work of humans teaching machines how to think. This work includes content moderation and labeling, is often conducted under exploitative conditions in the Global South, and remains hidden from users' view. In this episode, we discuss these issues with Adio Dinika, a Research Fellow at the Distributed AI Research Institute (DAIR), where he investigates the invisible labor behind AI systems and how it reflects the various inequalities within the AI industry.

We often perceive AI tools as entirely artificial, if not almost magical. In reality, the effectiveness and reliability of these systems depend significantly on the labor of humans who ensure that generative AI tools, for example, produce responses that are moderated and free from harmful or toxic content. High-quality training data is essential for building a high-performing large language model, and this data is made up of precisely labeled datasets—a task still carried out by human workers. However, this work is predominantly performed by people in the Global South, often under exploitative and unhealthy conditions, and remains largely invisible to end-users worldwide. The roles of these invisible workers, along with the challenges they face, represent some of the most visible signs of inequality within the AI and tech supply chain, yet they remain little discussed. In this episode of Machines That Fail Us, we dive into this issue with Adio Dinika, a Research Fellow at the Distributed AI Research Institute (DAIR), an international research center focused on the social implications of AI, founded by Timnit Gebru. Together with Dr. Dinika, we explore the hidden human labor behind AI systems and the real, human nature of artificial intelligence.