Artificial intelligence (AI) promises huge benefits. It is already helping to improve the diagnosis and treatment of cancers and other medical conditions. It is making education more accessible and is fundamental to self-driving cars. For humanitarian groups, it brings new tools in the fight against modern slavery to uphold human rights.

But for all the benefits, there is a darker side to AI. Advanced computing can help us model weather patterns and better respond to climate risks, but the models are energy-hungry and come with climate impacts of their own. Similarly, while AI could help combat slavery, it is built on exploitation that is reinforcing global inequality.

This digital exploitation is largely overlooked in the debate about the social implications of advanced technologies. People often focus on the dangers of using AI — including job losses from automation and the potential for bias in decision making — rather than the negative impacts associated with its creation.

With the introduction of the Corporate Sustainability Due Diligence Directive (CSDDD) in the European Union, requiring greater vigilance across the supply chain, companies may soon be pushed to look upstream as well as downstream in efforts to deploy AI responsibly.

AI’s Supply Chain: Millions Are Working in Digital Sweatshops to Train AI for Big Tech Companies

None of us is born knowing the difference between a cat and a dog. We learn to tell them apart when those around us point out cats and dogs. However sophisticated an AI algorithm, it also needs teaching. People need to collect text and images, label the content — indicating this is a dog and that is a cat, and so on — and feed it into the AI model so the technology can identify similar shapes and patterns in unlabelled images. We then check that the model identifies cats and dogs correctly, improving its accuracy.

It's an iterative process of refining AI models, with people at the core known as humans-in-the-loop, and it's one that is very costly. AI models need access to a library of at least 150 to 250 sample images to begin to reliably identify a single species or object. Some studies suggest it takes 1,000 or more images per object. The cost soon mounts up, particularly to train a general model such as ChatGPT.

Read the full article about AI’s supply chain by Sarah Walkley at TriplePundit.