There’s a story out this week on the BBC website about dialogue and gender in Game of Thrones. It uses data generated by artificial intelligence (AI) — specifically, machine learning — and it’s a good example of some of the challenges that journalists are increasingly going to face as they come to deal with more and more algorithmically-generated data.
Information and decisions generated by AI are qualitatively different from the sort of data you might find in an official report, but journalists may fall back on treating data as inherently factual.
Here, then, are some of the ways the article dealt with that — and what else we can do as journalists to adapt.
Margins of error: journalism doesn’t like vagueness
The story draws on data from an external organisation, Ceretai, which “uses machine learning to analyse diversity in popular culture.” The organisation claims to have created an algorithm which “has learned to identify the difference between male and female voices in video and provides the speaking time lengths in seconds and percentages per gender.”
Crucially, the piece notes that:
“Like most automatic systems, it doesn’t make the right decision every time. The accuracy of this algorithm is about 85%, so figures could be slightly higher or lower than reported.”
It’s the 8th year of the awards. This year the “Best data journalism team” category has been divided into two categories: small and large teams, with the “Small newsrooms (one or more winners)” category making way for the change.
In Hungary, not-for-profit news site Átlátszó has launched a full-time data team to create a wide range of data visualisations and data-driven stories. Amanda Loviza spoke to data journalist Attila Bátorfy about his plans to have Átló raise the quality of data journalism in Hungary.
Átlátszó was created in 2011 as Hungary’s first crowd-funded independent investigative news site, with a stated goal of holding the powerful accountable.
Data journalist Attila Bátorfy joined the site two and a half years ago. It was not long before he told editor-in-chief Tamás Bodoky that the site needed a whole separate team to produce higher quality data visualisations. Continue reading →
The latest in my series of FAQ posts follows on from the last one, in response to a question from an MA student at City University who posed the question “Do you think that an increase in algorithmic input is leading to a decline in human judgement?”. Here’s my response.
Does an increase in computation lead to a decline in human input?
Firstly, it’s important to emphasise that the vast majority of data journalism involves no algorithms or automation at all: it’s journalists making calculations, which historically they would have done manually.
You mention the possibility that “an increase in computation leads to a decline in human input”. An analogy would be to ask whether an increase in pencils leads to a decline in human input in art. Continue reading →
The latest in my series of FAQ posts comes in response to questions from a number of MA students at City University who emailed to ask “Can data journalism improve the world?”. Here’s my response, along with some follow-up questions and answers.
Can data journalism improve the world?
I wouldn’t be involved in data journalism if I didn’t think it could improve the world! But more broadly, I think journalism as a whole improves the world, whether that’s data journalism or not. (In fact, the whole reason I got involved in data journalism was because I believed it had the biggest potential to help journalism – particularly investigative journalsm – and, by extension, improve the world.) Continue reading →
In July an aggregator of data journalists from Spain and Latin America was launched under the name Periodista de Datos. Four months later, Maria Crosas Batista interviewed Félix Arias, project lead with Miguel Carvajal, to find out more about how the project came about — and where they plan to take it next.
Satisfying a need for up-to-date information in one place
This project came as the result of a specific need of journalists (and professors) driving the Innovation in Journalism MA (MIP) at the Miguel Hernández University (Elche, Spain).
Félix and Miguel were looking for a tool to use in their lessons that could show the potential of data journalism, as well as outstanding projects, to their students. Continue reading →
In an extended extract from the forthcoming second edition of the Data Journalism Handbook, I look at the different types of impact that data journalism can have, and how can better think about it.
If you’ve not seen Spotlight, the film about the Boston Globe’s investigation into institutional silence over child abuse, then you should watch it right now. More to the point — you should watch right through to the title cards right at the end.
In an epilogue to the film — this is a story about old-school-style data journalism, by the way — a list scrolls down the screen. It details the dozens and dozens of places where abuse scandals have been uncovered since the events of the film, from Akute, Nigeria, to Wollongong, Australia.
But the title cards also cause us to pause in our celebrations: one of the key figures involved in the scandal, it says, was reassigned to “one of the highest ranking Roman Catholic churches in the world.”
This is the challenge of impact in data journalism: is raising awareness of a problem “impact”? A mass audience, a feature film? Does the story have to result in penalties for those responsible for bad things? Or visible policy change? Is all impact good impact? Continue reading →