In September I took part in a panel at the African Journalism Education Network conference. The most interesting moment came when members of the audience were asked if they didn’t use AI — and why.
Last month I wrote about destination and journey prompts, and the strategy of designing AI prompts to avoid deskilling. In some situations a third, hybrid approach can also be useful. In this post I explain how such hybrid destination-journey prompting works in practice, and where it might be most appropriate.
Continue readingIn the second part of this FAQ (first part here), I respond to more answers to questions from a Turkish PR company (published on LinkedIn here)…
There are three core skills I always begin with: sorting, filtering, and calculating percentages (proportion and change). You can do most data journalism stories with those alone.
Alongside those basic technical skills it’s important to have the basic editorial skills of checking a source against other sources (following up your data by getting quotes or interviews), and being able to communicate what you’ve found clearly for a particular audience.
Continue readingIn the latest FAQ, I’m publishing here answers to some questions from a Turkish PR company (published on LinkedIn here)…
There have been so many major transformations in the last 15 years. Mobile phones in particular have radically transformed both production and consumption — but having been through all those changes, AI feels like a biggest transformation than all the changes that we’ve already been through.
It’s not just playing a role in transforming the way we produce stories, it’s also involved in major changes around what happens with those stories in terms of how they are distributed, consumed, and even how they are perceived: the rise of AI slop and AI-facilitated misinformation is going to radically accelerate the lack of trust in information (not just the media specifically). I’m being careful to say ‘playing a role’ because of course the technology itself doesn’t do anything: it’s how that technology is designed by people and used by people.
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Decisions around what data to visualise and how to visualise it involve a range of ethical considerations and challenges, and it is important to emphasise that data visualisation is an editorial process just as much as any other form of factual storytelling.
Journalists and designers employ a range of rhetorical devices to engage an audience and communicate their story, from the choice of the chart and its default views or comparisons, to the use of colour, text and font, and animations and search suggestions (Segel and Heer 2011; Hullman & Diakopoulos 2011).
The chart that a journalist chooses to visualise data plays a key role in suggesting the type of story that is being told, and what the user might do with the data being displayed.
If a pie chart is chosen then this implies that the story is about composition (parts of a whole). In contrast, if a bar chart is used then the story is likely to be about comparison.
Line charts imply that the reader is being invited to see something changing over time, while histograms (where bars are plotted along a continuum, rather than ranked in order of size) invite us to see how something is distributed across a given scale.
Scatterplots — which plot points against two values (such as the cancer rate in each city against the same city’s air pollution) — invite us to see relationships.
Continue readingAlthough the term ‘data journalism’ is a relatively recent one, popularised around 2010, data has been part of journalism throughout its history, from early newsletters covering stock prices and shipping schedules in the 17th century, to The Guardian’s 1821 first edition front page table of school spending, US investigations of politicians’ travel expenses in the 1840s and campaigning factchecking of lynching in the 1890s.
The introduction of computers into the newsroom in the 20th century added a new dimension to the practice. After some early experimentation by CBS News in predicting the outcome of the 1952 presidential election by applying computer power to data, a major breakthrough came in the 1960s with Philip Meyer’s use of databases and social science methods to investigate the causes of riots in Detroit.
Continue readingLook at the following chart showing the gender of teachers in UK schools, based on data on teacher headcounts. Notice anything wrong? (There are at least two problems)
The most obvious problem is that the chart appears to be ‘comparing apples with oranges’ (things that aren’t comparable). Specifically: “male”, “female”, and “unknown” are similar categories which can fairly be compared with each other, but “total” is a wider category that contains the other three.
I’ve used a pie chart here to make it easier to spot: we expect a pie chart to show parts of a whole, not the whole as well as its parts.
But the same problem should be obvious from the same data in a table before visualising it:
The table shows us that we have both a “Total” and a “Grand Total”. This is a red flag. There can only be one total, so if there’s more than one that’s a strong sign of double counting.
Why is this happening? We need to take a look at the data.
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O prompting de função envolve atribuir um função específico à sua IA. Por exemplo, você pode dizer “Você é um correspondente experiente de educação” ou “Você é o editor de um jornal nacional britânico” antes de delinear o que está a pedir que façam. Quanto mais detalhes, melhor.
Há pesquisas contraditórias sobre a eficácia do prompting de função, mas no nível mais básico, fornecer um papel é uma boa maneira de garantir que você fornece contexto, o que faz uma grande diferença na relevância das respostas.
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Almost all conversations around AI come down to these hopes and fears: that at its best AI can help us to reflect on our humanity. At its worst, it can lead us to forget it — or subjugate it.
When AI is dismissed as flawed, it is often through a concern that it will make us less human — or redundant.
The problem with this approach is that it can overlook the very real problems, and risks, in being human.
When people talk about the opportunities in using AI, it is often because they hope it will address the very human qualities of ignorance, bias, human error — or simply lack of time.
The problem with this approach is that it overlooks the very real problems, and risks, in removing tasks from a human workflow, including deskilling and job satisfaction.
So every debate on the technology should come back to this question: are we applying it (or dismissing it) in a way that leads us to ignore our humanity — or in a way that forces us to address our very human strengths and weaknesses?
Marie Gilot says the explainer is dead. Because AI.
“Today, our readers query AI for all that stuff,” she writes. “They like the AI answers well enough and they don’t click on article links.”
Here’s the type of content losing to AI: explainers, how-tos, evergreens, aggregated news, resource lists, hours of operation for government offices, recipes.
Gilot is right, of course. But only partly.
It’s right that the commercial imperative to produce explainers — low cost, high traffic — is going to come under severe challenge at one end.
But that doesn’t mean the explainer is dead. It just means they need to have a reason to fight for their life beyond money.
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