Training an AI machine with a representative data set is important, otherwise it can generate bias
are impressive places. The cashier-less shops, which first opened in Seattle in 2018, allow app-wielding customers to pick up items and simply walk out with them. The system uses many sensors, but the bulk of the magic is performed by cameras connected to ansystem that tracks items as they are taken from shelves. Once the shoppers leave with their goods, the bill is calculated and they are automatically charged.
Some data could be generated by Amazon’s own staff, who were allowed into test versions of the shops. But that approach took the firm only so far. There are many ways in which a human might take a product from a shelf and then decide to choose it, put it back immediately or return it later. To work in the real world, the system would have to cover as many of those as possible.
Such deficiencies are, at least in theory, straightforward to fix . Other sources of bias can be trickier to remove. In 2017 Amazon abandoned a recruitment project designed to hunt throughs to identify suitable candidates when the system was found to be favouring male applicants. The post mortem revealed a circular, self-reinforcing problem. The system had been trained on thes of previous successful applicants to the firm.
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