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.
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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.
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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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That’s because both types of story involve ranking as a piece of data analysis.
We might rank the number of specialist teachers in the country’s schools, for example, in order to tell either of the following stories:
The first story reveals which subject has the most teachers — it is a ranking angle because it ranks teachers by subject.
The second story reveals the simple fact that variation exists, without focusing on any particular subject.
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In creative work, for example, you might be looking for an idea, or aiming to produce a story or image. In journalism or learning, a ‘destination’ might be key facts, or an article or report.
But prompts that focus only on those destinations are less likely to help us learn, more likely to deskill us — and more likely to add errors to our work.
To avoid those pitfalls, it is better to focus on how we get to those destinations. What, in other words, are the journeys?
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One of the most productive ways of using generative AI tools is role playing: asking Copilot or ChatGPT etc. to adopt a persona in order to work through a scenario or problem. In this post I work through four of the most useful role playing techniques for journalists: “rubber ducking”, mentoring, “red teaming” and audience personas, and identify key techniques for each.
Role playing sits in a particularly good position when it comes to AI’s strengths and weaknesses. It plays to the strengths of AI around counter-balancing human cognitive biases and ‘holding up a mirror’ to workflows and content — and scores low on most measures of risk in using AI, being neither audience-facing nor requiring high accuracy.
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