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The Finnish tech firm Metroc\u00a0recently began using prison labour to train a large language model to improve artificial intelligence (AI) technology<\/a>. For 1.54 euros an hour prisoners answer simple questions about snippets of text in a process known as data labelling.<\/p>\n Data labelling is often outsourced to labour markets in the Global South where companies can find workers who are fluent in English and willing to work for low wages.<\/p>\n Due to the lack of Finnish speakers in these countries, however, Metroc has tapped into a local source of cheap labour. Were it not for the prison labour program, Metroc would likely be hard-pressed to find Finns willing to take data-labelling jobs that pay a fraction of\u00a0the average salary in Finland<\/a>.<\/p>\n These cost-cutting strategies not only highlight the significant amount of human labour still required to fine tune AI, but they also raise important questions about the long-term sustainability of such business models and practices.<\/p>\n The ethical ambiguity of prison labour-sourced AI is part of a larger story about the\u00a0human cost behind AI\u2019s significant growth<\/a>\u00a0in recent years. One issue that has become more evident over the past year revolves around the\u00a0question of labour<\/a>.<\/p>\n Leading AI firms are not denying their use of outsourced and low-wage labour to do work like\u00a0data labelling<\/a>. However, the hype around tools like OpenAI\u2019s ChatGPT has drawn attention away from\u00a0this aspect of the technology\u2019s development<\/a>.<\/p>\n As researchers, including myself, are trying to understand\u00a0the perceptions and use of AI in higher education<\/a>, the ethical problems associated with current AI models continue to pile up. These include the\u00a0biases that AI is prone to reproducing<\/a>, the\u00a0environmental impact of AI data centres<\/a>, and\u00a0privacy and security concerns<\/a>.<\/p>\n Current practices of outsourcing data labelling work expose\u00a0an uneven global distribution of AI\u2019s costs and benefits<\/a>, with few proposed solutions.<\/p>\n The implications of this situation are twofold.<\/p>\n First, the massive amount of human labour that is still required to shape the \u201cintelligence\u201d of AI tools should give users pause when evaluating the outputs of these tools.<\/p>\n Second, until AI firms take serious steps to address their exploitative labour practices, users and institutions may want to reconsider the so-called values or benefits of AI tools.<\/p>\nAI\u2019s labour problem<\/strong><\/h4>\n