We therefore use a hybrid of the two approaches. We first use GPT-4 to categorise each of the 19,281 tasks in the O*NET database in several different respects that we consider to be important determinants of whether the task can be performed by Al or not. These were chosen following an initial analysis of GPT-4's unguided assessment of the automatability of some sample tasks, in which it struggled with some assessments. This categorisation enables us to generate a prompt to GPT-4 that contains an initial assessment as to whether it is likely that the task can or cannot be performed by Al. In the final stage, we ask GPT-4 to categorise the type of Al tool that could be used to perform the task, estimate the time saving and give an assessment of whether it would be cost effective to deploy the tool. We then merge this information with UK Labour Force Survey data to calculate overall time savings given the numbers in each occupation group.
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