T
he world is gearing towards an ‘automated economy’ where machines relying on artificial intelligence (AI) systems produce quick, efficient and nearly error-free outputs. However, AI is not getting smarter on its own; it has been built on and continues to rely on human labour and energy resources. These systems are fed information and trained by workers who are invisibilised by large tech companies, and mainly located in developing countries.
A machine cannot process the meaning behind raw data. Data annotators label raw images, audio, video, and text with information that trains AI and Machine Learning (ML) models. This, then, becomes the training set for AI and Machine Learning (ML) models. For example, an large-language models (LLM) cannot recognise the colour ‘yellow’ unless the data has been labelled as such. Similarly, self-driving cars rely on information from video footage that has been labelled to distinguish between a traffic sign and humans on the road. The higher the quality of the dataset, the better the output and the more human labour is involved in creating it.
