‘I’m bullish on the capabilities and opportunities that machine learning presents to editorial work, but also cautious enough to remind readers that machine learning is not the answer to every journalistic task.’
The Columbia Journalism Review explains machine learning with examples of how it’s used in newsrooms. Author Nick Diakopoulos describes four ‘flavours’ of machine learning, varying by the amount of input by humans.
In one, supervised learning, the data to ‘train’ the system needs to be labelled.
The labels can be as simple as naming a document ‘interesting’ or ‘uninteresting,’ but need to be there as clues. The labels instruct the algorithm hiw to assess future documents as a match for ‘interesting.’
The other three require less human input, in varying degrees. One of the challenges in journalism, he suggests, is matching the flavour to the objective, beginning with the question, will any be useful?
Diakopoulos points to bias and uncertainty as potential pitfalls, again using examples from news work.
What is machine learning and why should I care?
COLUMBIA JOURNALISM REVIEW | April 25, 2019 | by Nicholas Diakopoulos