Artificial intelligence and journalism: a race with machines

The term Artificial Intelligence (AI) is a somewhat catch-all term that refers to the different possibilities offered by recent technological developments. From machine learning to natural language processing, news organisations can use AI to automate a huge number of tasks that make up the chain of journalistic production, including detecting, extracting and verifying data, producing stories and graphics, publishing (with sorting, selection and prioritisation filters) and automatically tagging articles. These systems offer numerous advantages: speed in executing complex procedures based on large volumes of data; support for journalistic routines through alerts on events and the provision of draft texts to be supplemented with contextual information; an expansion of media coverage to areas that were previously either not covered or not well covered (the results of matches between'small' sports clubs, for example); optimisation of real-time news coverage; strengthening a media outlet's ties with its audiences by providing them with personalised context according to their location or preferences; and more. But there is a flipside to the coin: the efficiency of these systems depends on the availability and the quality of data fed into them. The principle of garbage in, garbage out (GIGO), tried and tested in the IT world, essentially states that without reliable, accurate and precise input, it is impossible to obtain reliable, accurate and precise output.