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Content type: Explainer
Behind every machine is a human person who makes the cogs in that machine turn - there's the developer who builds (codes) the machine, the human evaluators who assess the basic machine's performance, even the people who build the physical parts for the machine. In the case of large language models (LLMs) powering your AI systems, this 'human person' is the invisible data labellers from all over the world who are manually annotating datasets that train the machine to recognise what is the colour…
Content type: Examples
Companies like the Australian data services company Appen are part of a vast, hidden industry of low-paid workers in some of the globe's cheapest labour markets who label images, video, and text to provide the datasets used to train the algorithms that power new bots. Appen, which has 1 million contributors, includes among its clients Amazon, Microsoft, Google, and Meta. According to Grand View Research, the global data collection and labelling market was valued at $2.22 billion in 2022 and is…
Content type: Examples
French data protection agency CNIL has fined Amazon's French warehouse management unit €32 million, or about 3% of its turnover, for its "excessively intrusive" surveillance of the performance of its thousands of staff. The system relied on data collected from the scanners warehouse staff use to process packages. CNIL said the surveillance placed workers under continuous pressure and forced them to justify absences, as the scanners timed inacctivity to the second and also penalised workers for…