The problem with annotation. Human labour and outsourcing between France and Madagascar

Abstract

Artificial intelligence advancements have reignited job displacement debates that focus on how the use of artificial intelligence affects labour, without considering how the production of this technology influences labour division. The generalisation of machine learning has created an increased demand for outsourced data workers. Outsourcing companies and crowdwork platforms are both used to generate, annotate, and enrich data. This data tasks are performed by workers from low-income countries, who often earn poverty wages. As with traditional outsourcing, workers must integrate complex multinational subcontracting networks. In this article, we examine how France outsources artificial intelligence-related tasks to workers in the African island nation of Madagascar. For our study, we interviewed 26 data workers, eight employees of French start-ups, and conducted secondary research on two artificial intelligence systems – a canteen checkout terminal and an algorithm to detect shoplifters in stores. The data collected allowed us to reconstruct an end-to-end artificial intelligence production value chain, revealing the need for data classification and artificial intelligence problematisation. Commercial artificial intelligence, therefore, does not displace employment by automating service jobs. Rather, by delocalising labour into the Global South, it lengthens the externalisation chain.