PEER is a mnemonic (based on a previous post) for remembering the following four types of source:

• Power
• Expertise
• ‍ Experience
• Representative

Each type of source brings something different to the story: voices of power primarily (but not solely) answer questions about action: what was or is being done, what should or would be done about a particular issue. These are easily the most commonly quoted sources in news reporting.

People with expertise can answer the “why” and “how” questions — and are often more likely to speak to journalists — while those with experience can verify or validate (put a human face to) events. Representatives can speak to the wider impact or significance of an issue, or represent community sentiment about it.

Making each type of source explicit allows us to think about what those roles really mean — and identify less obvious ideas for sources with power, expertise, experience or representative qualities.

Power isn’t just political

The most obvious examples of sources with power are politicians. Their power comes primarily from the ability to make and shape laws and other rules, to spend money, and cut budgets. But by thinking about power in general, you can break power down into at least five sub-categories of source:

• Political power more broadly: not just those in government and opposition, but local government, police and crime commissioners, mayors and other elected and unelected officials
• Legal power: police, regulators, sports governing bodies and professional membership bodies all have the power to enforce rules
• Financial power: company bosses, investors, etc.
• Cultural power: celebrities, high profile experts, pundits, authors, former sportspeople, editors and media bosses, and anyone with a large following
• Mobilisation power: those able to mobilise large numbers of people include unions, the armed forces, campaign groups and charities, the media and influential individuals

With that list, you can start to think of a much wider range of potential people to approach on a story than the usual suspects.

Expertise isn’t just academic

The same applies to experts: while universities might be the most obvious place to look for expertise, it’s just one option:

• Academic expertise: those who research and teach in the field
• Non-academic research expertise: those who research topics outside of academia, such as statisticians, charity researchers and analysts, and authors.
• Professional expertise: those who hold authority on the basis of working in the field at a senior level, often for a long time
• The expertise of achievement: those who have won awards and/or broken records in the field (for example, an award-winning barista or former sportsperson)
• Experiential expertise: those with extensive experience of a process or issue (for example someone who has patented dozens of inventions)

The Guardian feature The best £20 you’ll ever spend: 16 affordable essentials the experts swear by provides a good example of the diversity of expertise journalists draw on. Writing in the newspaper’s Inside Saturday newsletter, Daisy Schofield explained how she identified sources for the piece:

“When it came to fashion, I immediately thought of Chioma Nnadi, British Vogue’s head of editorial content, who has impeccable taste. My pet-related expert had to be the dog behaviourist Louise Glazebrook. Her book, The Book Your Dog Wishes You Would Read, was a lifeline when I got my puppy.

“Kelly Holmes was another obvious choice. The double Olympic champion has recently turned her attention to home fitness – including for beginners – so I knew she would be able to advise on work-out equipment for the exercise-averse.”

Other sources included founders or CEOs of companies, more authors, and a prominent menswear designer.

Representatives are not always elected

Representatives take two broad forms: those who act as a voice for a wider group of people, and those who have an ‘ear to the ground’ and can summarise what is being said. Examples include:

• Unions
• Professional associations (not to be confused with regulators)
• Industry bodies and networks (e.g. Chambers of Commerce)
• Charities, campaign groups and pressure groups
• Owners, moderators and founders of groups and forums on social media and offline, and some specialist media
• Religious leaders, community leaders (the two are not the same thing)
• Elected representatives

There are subtle and importance differences between different types of representatives: a union, for example, can negotiate formally on behalf of its members, and union leaders are elected to their positions.

A Facebook group owner is in a very different position: they can give an indication of what members’ main concerns appear to be — but do not speak on their behalf. In some cases they may play more of the role of ‘expert’ on those concerns rather than that of formal representative, but this remains a useful alternative when a group has no formal representation at all.

Experience is hard to find

The hardest type of source for journalists to find is the person with some experience of the issue they are reporting on. These sources often answer one of two key questions in a story: “What happened (or what happens)?” and “What is it like to be affected by this issue?”

They include:

• Eye witnesses to a news event (e.g. an accident, natural disaster, attack, etc.)
• People affected by an issue (e.g. a medical condition, cuts, addiction, bereavement, etc.)
• People who have experienced something similar in the past (e.g. competing in a competition, a crime, etc.)

This requires some empathy both to imagine the types of people who might be affected, and how they might be reached.

A common strategy, for example, is to imagine organisations a person might come into contact with and approach those instead, asking if they can put you in touch with a case study.

Journalists will often approach charities that represent people in certain situations, or with particular medical conditions, for example; or they might speak to campaign groups and lawyers who specialise in particular types of cases or laws.

A more direct strategy is to find sources by looking on social media, specialist forums and groups, or crowdfunding pages. In this approach you are trying to imagine spaces where those people might share their experiences, gather to coordinate action, access support, raise money, or keep in touch with former colleagues.

Sources can occupy more than one category — but their role shouldn’t

You’ll notice that some individuals can appear in more than one category:

• An author can be the voice of expertise, or exercise cultural power (campaigning for change for example).
• A former footballer can describe what it is like to walk out at a cup final, or they can provide expert insights on the tactics of the finalists.
• A politician might be quoted because they have responsibility for a sector, or in their capacity as a representative of their constituency. They might be asked to give expert insights into a political process, or share their experiences of being abused on social media.

It is important, then, to clarify what role a source is playing in your story, and shape your questions accordingly. A politician can be the voice of power, or the voice of their constituents, for example, but it would probably feel confusing or lazy — or both — for them to asked questions that mean they play both roles within the same story.

You should obviously also consider the reliability of sources, and how they relate to each other: make sure to identify and seek out independent voices and sources that disagree with each other to improve the accuracy and objectivity of the story overall.

Audit your sources: PEER’D?

Once classified in this way, you can also use the PEER framework to audit the sources you tend to seek out when reporting, or quote.

Do you tend to mostly think of, or quote, people in positions of power, for example? Are representatives and case studies more of a blind spot?

More specifically, you might use an audit to identify what voices of power (political, cultural, financial, etc.) are loudest in your reporting, or what categories of expertise you have a habit of seeking out.

Are there types of sources you could seek out more which would improve your reporting or give you a wider range of story options? Sources you need to quote less often?

It is worth measuring the diversity of those sources in your audit, too (if it works for you, make the acronym PEER’D) — the 50:50 project, for example, already provides a way to “help production teams improve the range of their on-screen contributors in terms of gender, disability and ethnicity”, and could be integrated into this to identify if there is a particular lack of diversity when generating ideas for certain types of sources.

However you approach it, having a clear understanding of the types of sources you tend to draw on most can help you widen your range of sources, generate more story ideas, have a more diverse range of voices in your reporting, and make your journalism more original.

You can read more about the different roles that those sources play in Here’s a framework to help fill the ‘human gap’ in your story.

Being curious: how often is this happening? How much has it increased?

Curiosity is the first quality I identified in my series on the 7 habits of successful journalists — and FOI is a great way to hone that.

One good way to get started with FOI is to identify an event or problem that you’ve read about, and get curious about it: how many times is that event happening? How much is that problem costing? These are perfect questions for FOI.

Asking for those figures over a period of time also allows you to establish whether those events or costs are becoming more frequent or increasing.

This sort of request can work particularly well when prompted by the experiences of you and your contacts. If you discharged yourself from hospital because there were no staff available to do so, for example, how many other patients are having to do the same?

You might also just be curious about how many times particular things happen — police being disciplined, or lives being saved.

One thing to consider when asking for data on incidents or costs is how things are classified and counted. FOI requests about crimes involving clowns, for example, provided a string of quirky stories back in 2014, but as Richard Osley wrote at the time, the figures included crimes where someone was called a “clown”, had a clown tattoo or wore “clown-like shoes”. Locations and objects using the keyword were also included.

Official definitions of certain terms can be useful to know (or ask for). If you’re asking about frequent callers to the ambulance service, for example, it helps to know that they define that as “A patient aged 18 years or over, calling from a private dwelling [that] generates 5 or more incidents in a calendar month” — so it wouldn’t include younger callers or those calling from phone boxes.

Being sceptical: checking a claim

How can you develop scepticism as a journalist? Find an official statement and ask what evidence there is behind it.

Protests have proven particularly fruitful subject matter for this: official bodies often leave vital information out of public statements that can be unearthed through FOI.

For example police force injury figures from protests have often included minor injuries unrelated to the protestors themselves.

As well as asking for breakdowns of figures, you can also ask for internal correspondence and reports to compare those to the public story being told.

A good example of this is the East Bay Express‘s use of FOI to obtain internal documents recording discussions about a surveillance hub called the Domain Awareness Center (DAC). Publicly, the surveillance was pitched as aimed at tackling gang and street violence, but FOI revealed that privately this wasn’t the real objective:

“While the emails reveal a great deal about the DAC, they are also notable for what they do not talk about. Among the hundreds of messages sent and received by Oakland staffers and the city’s contractor team responsible for building the DAC, there is no mention of robberies, shootings, or the 138 homicides that took place during the period of time covered by the records. City staffers do not discuss any studies pertaining to the use of surveillance cameras in combating crime, nor do they discuss how the Domain Awareness System could help OPD with its longstanding problems with solving violent crimes. In more than 3,000 pages of emails, the terms “murder,” “homicide,” “assault,” “robbery,” and “theft” are never mentioned …

“City staffers repeatedly referred to political protests as a major reason for building the system.”

Employing empathy: investigating private companies

Empathy is an act of imagination, not sympathy. And when it comes to non-public bodies, you’ll need to use this to imagine at what points they come into contact with public systems.

You cannot send an FOI request to a company or a charity because they are not subject to the FOI Act. But you can use FOI to get information about those organisations through the public bodies that they have to deal with.

Accusations relating to poor or unsafe working conditions, for example, can be investigated by asking local emergency services about callouts to addresses owned by a particular company. Prisons might be run by private companies but you can FOI the health board for data on incidents, as The Ferret did.

Many companies are overseen by regulators who are subject to FOI. Charities are regulated by the Charity Commission — but also, if they are receiving money from public bodies to provide services, contracts can be obtained through FOI along with any information they are required to provide. Requesting correspondence with public bodies is another strategy to consider.

In sport, games have to be policed and stadiums inspected by authorities. Police forces collect data on how many fans are at matches — making it possible to report stories on how many fans are staying away. Tax authorities investigate footballers’ finances; public bodies carry out doping tests and provide grants.

Put yourself in the company’s place and ask the following two questions:

1. What rules does this company have to follow (and which public bodies enforce those)?
2. Where does it get, or give, money from or to public bodies (including tax)?

Being creative: adapting to what you get (or don’t get)

In some cases you will not receive what you asked for in your FOI request because the organisation doesn’t collect that information.

This doesn’t mean that you don’t have a story: the fact that the organisation doesn’t collect that information might be a story in itself.

It is said that “what gets measured gets managed” — which also means that anything that’s not being measured is not being managed. If something is a problem and it isn’t being managed, then that’s newsworthy.

One set of FOI requests to health authorities, for example, “revealed how few have a grasp of their legal obligations” because they “could not provide details on the number or the location of section 140 beds in their region [and] could not say what the arrangements were for people who need a section 140 bed to actually receive one.”

Before you send an FOI request check which organisation is responsible for dealing with the events you are interested in. The Department for Education, for example, will not provide information on missing children because that is the responsibility of the National Crime Agency.

If you are sending requests to multiple bodies, such as all the police forces or councils, there’s also a good chance that some will fail to provide information. In this case the phrase “at least” is going to come in useful:

• Liberty Investigates‘ FOI story, for example, revealed that “At least 28 universities are now known to have launched disciplinary investigations against students and staff in connection with their Palestine activism since October 2023”. Lower in the story it adds that “Nearly 50 universities refused to respond”.
• The Guardian reported that “At least 511 people died in England [after the ambulance they called for took up to 15 hours to reach them]” (“Only three of England’s 10 regional ambulance services provided full-year figures for the last two years as requested.”)
• BBC Radio 5 Live‘s story Police pay out at least £22m to informants in five years was based on responses from 43 of the 45 territorial police forces, one of which only provided figures for three of those years.

More generally, be prepared to adapt your story to what the information says. If you were hoping to do a story on change, but the figures haven’t changed, adapt by changing the angle to focus on the current scale of the problem, or which areas have the biggest problems — or use interviews to find a story about concerns over a lack of improvement. Or if the figures are improving, use interviews to find out what’s working.

If the request is refused, look at stories about refusals to get ideas on how to tell a story about refusals or lack of transparency.

Being passionate: pick a topic you care about

A focus on “Freedom of Information law” and “public bodies” can be dry. But focus on music, sport, discrimination or education and you might start to see FOI differently.

The key is to focus on what makes you passionate.

The Guardian’s roundup of How students use FOI to investigate their universities is a great place to start for student journalists who are curious, angry or passionate about their own institution.

If music is your passion look at stories like DJ Mag’s story on police requests to remove UK drill content from TikTok, or The Register’s piece on the use of facial recognition at Download Festival.

In sport look at The Pitch Inspection’s stories on the lack of transparency around meetings with football bosses and Saudi officials, or the Daily Mail’s “grim list of Premier League match-day injuries you NEVER hear about!” Look at public bodies that operate in the sector, like Sport England, UK Anti Doping, or even the organising committee for a Commonwealth or Olympic Games.

Discrimination is a regular focus of FOI requests, from racist incidents in classrooms to how many officers found guilty of racism are sacked, from stalling on equal pay claims to the gap between top-paid male and female staff, from the “shocking disparity” faced by people with non-visible disabilities to the number of flights missed by disabled passengers. If you’re passionate about discrimination, FOI can be a powerful tool to shine a spotlight on it.

Being disciplined: make a plan to follow up on policies

One particularly clever use of FOI is to check what happens after a new policy has been announced, a new process introduced, or a new taskforce or group formed.

The impact won’t be immediate — so you’ll need to be organised in planning ahead for when evidence of its impact will be available to FOI.

Set a calendar reminder for a certain time after its introduction: it could be as short as six months (for a taskforce expected to meet monthly, for example) or as long as five years. You might aim to get the information in time for a key anniversary, for example (either of the announcement or an event that led to it) by asking for the information a month or two before it.

Once you’ve sent an FOI request, it’s also a good idea to set a reminder four weeks later to chase it up (the FOI tool WhatDoTheyKnow sends you one automatically).

And for multi-FOI projects many journalists will create a spreadsheet to keep track of all their requests and the status of each. FOI website Proactively Open have shared a template spreadsheet here.

Being disciplined and creative: use existing stories as recipes

Each of the stories linked above provides a recipe for your own FOI story, which can be adapted for your own purposes. Here are just five ways you can be more creative in coming up with ideas for FOI (you can develop discipline by generating ideas for each method systematically):

• Bring it up to date: can you ask for the same information, just for a more recent timescale?
• Change the location: can you ask for the same information for a different area? Locally? Nationally? Internationally? (Dozens of countries have FOI laws)
• Change the category: if the previous story was about car thefts, what about asking for the same information about bicycle thefts, or violent crime, or shoplifting?
• Change the body: if it was about police, why not ask for similar information from the fire or ambulance service?
• Change the angle: if the focus was scale, ask for information that allows you to focus on change, or ranking, and vice versa. Use the 7 angles listed here.

Being persistent: negotiate, appeal, demand

FOI can provide great opportunities to develop tenacity and persistence. If an organisation rejects your FOI, don’t treat that as the end of the process. Instead, see it as an opportunity practise key skills:

Can you negotiate? Consider why they have refused it, and how you or they might adjust so that those reasons no longer apply.

If the authority has refused your request because it would take too much time, for example, you can narrow the request to a shorter timeframe, or fewer questions. They are obliged under the FOI Act to help you do this, too, so you can ask them to help identify what comes under the time limit.

Similarly, if the refusal is because of privacy or confidentiality, you can ask for aggregate figures instead of individual incidents, and you can ask for material to be redacted (this should not be counted for the purposes of cost limits)

Can you demand? Some reasons for refusals (exemptions) are subject to a public interest test. You can ask them to provide evidence that they have conducted this – and one useful technique is to include this demand as part of your original request should they choose to refuse. The idea is that refusing your request should be harder than complying with it.

Can you appeal? All refusals can be appealed. At first you have to appeal internally. A surprising number of internal appeals are successful, but if it’s not then you would next appeal to the ICO, and even the courts. In most cases you’ll be arguing that either the public interest of releasing the information outweighs any private interests, or that an exemption has been incorrectly applied (e.g. it’s not a national security issue).

You’ll need to do some research into the exemptions that they’ve used – there’s plenty of material out there on these, and cases where they’ve been successfully (and not) appealed.

You can’t learn that? More ways to develop good habits

For other tips on developing curiosity, scepticism, persistence, discipline, creativity and passion, check out the posts in the series below:

A pirâmide invertida do jornalismo de dados mapeia o processo de utilização de dados na reportagem, desde a geração de ideias, passando pela limpeza, contextualização e combinação, até à comunicação. A fase final — a comunicação — apresenta uma série de opções: desde a visualização e sonificação até à personalização e ferramentas. Mas quais são as melhores práticas para cada uma?

(Também disponível em inglês, alemão e espanhol, russo e ucraniano).

1. Visualização

A visualização é normalmente a forma mais rápida de comunicar os resultados do jornalismo de dados: ferramentas gratuitas como Datawrapper e Flourish muitas vezes exigem apenas que você carregue os seus dados e escolha entre várias opções de visualização.

Quando utilizada corretamente, a visualização pode aumentar significativamente o engajamento — mas a velocidade não garante qualidade. O aumento do “chart junk” — elementos gráficos supérfluos que distraem do conteúdo — demonstra que a visualização é tão suscetível a efeitos superficiais quanto outras formas de jornalismo.

Existe uma longa tradição de princípios úteis da visualização impressa que continuam relevantes para gráficos, mapas e infográficos online:

• Concentre-se em poucos pontos de dados relevantes
• Evite efeitos 3D
• Garanta que o gráfico seja compreensível mesmo sem texto adicional

Muitas redações também desenvolveram as suas próprias diretrizes — desde o Visual Vocabulary do Financial Times e o Visual and Data Journalism Cookbook da BBC até ao Audiences Tableau Style Guide. Autores especializados como Dona Wong (The Wall Street Journal Guide to Information Graphics) e Alan Smith (How Charts Work) também oferecem dicas valiosas.

A visualização pode ser extremamente eficaz — mas é precisamente por isso que é importante agir de forma estratégica e, por exemplo, garantir que cada gráfico inclua um link para a sua fonte.

2. Narrar

Qualquer história de dados também pode ser contada com palavras. Para uma notícia simples, concentre-se no que os dados “revelam”: a dimensão de um problema; quem ou onde está melhor ou pior; desigualdade; relações; ou mudança.

Decida antecipadamente quais números são mais importantes para a sua história e use o menor número possível: o princípio do “menos é mais” aplica-se à escrita sobre dados tanto quanto à visualização.

Tente intercalar os parágrafos baseados em dados com secções que contenham citações ou informações de contexto. Isso evita a monotonia e pode fornecer contexto fundamental.

Boas histórias não nos dão apenas os factos do que está a acontecer, mas também por que acontece (através de especialistas) e por que é relevante (impacto humano). As seguintes perguntas ajudarão a fornecer contexto:

• O número representa uma grande proporção do total?
• É superior ao do ano passado ou comparado com outras localidades?
• Para que seria usado esse valor? Torne os números mais tangíveis convertendo-os numa base per capita ou diária.

Lembre-se de que números adicionais podem sempre ser disponibilizados aos leitores através de uma tabela, gráfico ou link para os dados completos.

Em alguns casos, os números podem ser omitidos completamente: os dados podem levá-lo a um estudo de caso que se torna a história em si…

3. Humanizar

Estudos de caso colocam um rosto humano nos números. Para o jornalismo audiovisual em particular, isso é essencial: histórias baseadas em dados em plataformas de vídeo e áudio normalmente começam com um rosto, ou uma voz no centro da história, não apenas para fornecer material audiovisual fundamental. Mas isso também pode funcionar bem online e nas redes sociais.

Quando a BBC Shared Data Unit trabalhou numa história sobre as falhas no tratamento de drogas para jovens, por exemplo, optou por iniciá-la com: “Três mulheres cujos filhos adolescentes morreram após se tornarem dependentes de drogas disseram à BBC que o sistema falhou com os seus filhos.”

Os dados podem ajudar a conduzir-nos a estudos de caso: ordenar uma folha de cálculo para encontrar a organização ou o local mais afetado por um problema pode mostrar-nos para onde direcionar as nossas investigações ou onde filmar.

4. Tornar social

A comunicação é um ato social, mas não são apenas as palavras e os elementos visuais que são sociais: os próprios dados também podem ser sociais. O Guardian demonstrou isso com particular sucesso ao cultivar uma comunidade ativa em torno do seu Data Blog (que registou maior engajamento do que o artigo médio do Guardian) e em torno da sua API. Também aproveitou as propriedades sociais da visualização com um grupo no Flickr.

Iniciativas de crowdsourcing destinadas a recolher dados também podem conferir uma dimensão social aos dados. O projeto de despesas dos deputados do Guardian e a tentativa de Charles Arthur de usar crowdsourcing para prever as especificações do iPad são exemplos disso. Mas há outros exemplos também — especialmente quando é difícil obter os dados de outra forma.

A narrativa social exige pensar em como capacitar e inspirar o público a contar as suas próprias histórias. Isso pode ser feito fornecendo uma plataforma, como o grupo Flickr do Guardian; pode ser fornecendo dados brutos; ou pode ser fornecendo uma forma de os leitores criarem e partilharem histórias personalizadas descobertas através da interatividade (veja abaixo). O 7 billion and you da BBC, por exemplo, permitiu aos utilizadores partilhar o seu próprio número nas redes sociais. Isso gerou milhares de histórias simples individuais, além da história interativa personalizada no site da BBC.

5. Sonificação/materialização

Em formatos de rádio ou podcast onde gráficos não são possíveis, os dados podem ser representados como som (sonificação) — o que muitas vezes torna o conteúdo mais fácil de compreender. Por exemplo:

• Planet Money: “U.S. Home Prices, Sung As Opera“
• The World: Música para ilustrar apagões numa reportagem
• Reveal: Estatísticas de sismos traduzidas em som

O podcast “Loud Numbers” usa esta técnica em todos os episódios, para histórias que vão desde a época de incêndios florestais no Canadá até à degustação de cerveja e à economia dos EUA. Também acolhe uma comunidade para “sonificadores”. Pode encontrar ferramentas para converter dados em música no Data Sonification Toolkit.

Alternativamente, os dados podem ser transformados — materializados — em algo tangível que possa ser experienciado fisicamente. As duas formas mais comuns são como objetos num espaço expositivo — por exemplo, uma pilha de grãos de arroz simbolizando um determinado número de pessoas — e objetos decorativos ou de vestuário, como uma pulseira ou trabalho de crochet.

6. Personalizar/utilizar

Os dados abrem todo o tipo de possibilidades em torno da interatividade em geral — e da personalização em particular.

Pelo menos quatro tipos de personalização podem ser identificados na narrativa de dados:

• Personalização demográfica (por exemplo, personalização geográfica ou financeira)
• Correspondência de crenças (por exemplo, políticas partidárias)
• Personalização de interesses (por exemplo, páginas iniciais personalizadas)
• Personalização de conhecimento (por exemplo, nível de detalhe, extensões de navegador e jogos)

A personalização demográfica permite que um utilizador veja como “pessoas como eu” são afetadas por um problema ou política. A personalização geográfica é o exemplo mais comum: pode ser fornecida através de mapas interativos — mas também pode ser feita através de menus dropdown ou caixas de pesquisa onde o utilizador é convidado a inserir o seu código postal ou outras informações geográficas para descobrir como uma determinada questão está a desenrolar-se na sua cidade. Dados de geolocalização ou de redes sociais também podem ser utilizados.

As calculadoras personalizam a narrativa em torno de outras variáveis demográficas: salário, estado civil e padrões de consumo, por exemplo, podem ser usados para contar uma história sobre como um novo orçamento afetará o utilizador. O formato de calculadora também foi usado para personalizar histórias sobre inflação, obesidade, os ganhos de estrelas desportivas e celebridades, e término de relacionamentos.

A correspondência de crenças é mais frequentemente utilizada durante eleições, quando muitas equipas de dados produzem questionários interativos para ajudar a identificar o partido cujas políticas correspondem às suas.

A personalização de interesses tende a ocorrer ao nível do site, em vez do nível da história, onde uma página inicial pode ser personalizada para se concentrar nos interesses do utilizador (como a sua equipa desportiva favorita).

Mas também é possível ao nível da história, fornecendo diferentes níveis de detalhe com base no grau de interesse do utilizador no assunto. No passado, o “jornalismo estruturado” tornou isso possível. A IA agora torna esta abordagem muito mais fácil do ponto de vista técnico.

A personalização de conhecimento é muito semelhante: envolve personalizar a informação com base no conhecimento existente de uma pessoa. Um questionário é um exemplo simples: no final, as lacunas no conhecimento do utilizador são identificadas e preenchidas. Ou os sites podem pedir ao utilizador que descreva o seu nível de conhecimento — ou usar o seu histórico de leitura — antes de apresentar uma versão da história adequada a esse nível.

Os jogos muitas vezes fornecem implicitamente este tipo de personalização, pois o conhecimento e as crenças do utilizador moldam as decisões que toma, que por sua vez moldam a história. Decisões sobre um orçamento, ou no papel de um motorista da Uber, ajudam a “preencher as lacunas” sobre como poderiam ser as experiências resultantes dessas escolhas.

As extensões de navegador e a realidade aumentada (RA) são exemplos mais raros: ambas adicionam uma camada de conhecimento ao mundo do utilizador. Quem navega na Amazon com a extensão do The Markup instalada, por exemplo, verá os produtos de marca própria da plataforma “revelados”. E quem usa uma ferramenta de RA verá uma camada virtual adicionada aos seus arredores físicos.

Algumas destas formas têm sido subdesenvolvidas devido aos limites de tempo de desenvolvimento e de informação sobre os utilizadores, mas a IA generativa reduz as barreiras em ambos os casos: a vibe coding torna mais fácil do que nunca desenvolver páginas web interativas, dashboards e ferramentas; e os grandes modelos de linguagem podem agora “traduzir” facilmente informação com base no conhecimento, interesses ou demografia de um utilizador. O principal desafio ético da personalização é como equilibramos duas tensões concorrentes: envolver o público com o que é importante e garantir que as pessoas não fiquem isoladas das experiências de quem é diferente delas.

Se tiver exemplos interessantes de narrativa de dados para acrescentar aos listados acima, informe-me nos comentários ou no LinkedIn.

Traduzido com Claude Sonnet 4.5. Por favor, diga-me se encontrar algum erro nos comentários ou através do LinkedIn.

Os projetos de jornalismo de dados envolvem várias etapas, cada uma apresentando seus próprios desafios. Para ajudar a compreendê-las, criei o que chamei de ‘Pirâmide Invertida do Jornalismo de Dados’. Ela delineia as etapas que precisam ser consideradas à medida que a matéria avança desde a conceção inicial até a comunicação dos resultados, e como elas se relacionam entre si. Abaixo, explico cada etapa, identifico questões a considerar conforme o projeto avança e ofereço conselhos e dicas sobre como enfrentá-las.

(Também disponível em Inglês, Alemão, Espanhol, Finlandês, Russo e Ucraniano.)

Etapa 1: Conceber

O primeiro desafio que um jornalista enfrenta é conceber uma ideia viável para uma matéria baseada em dados.

Existem pelo menos sete maneiras pelas quais os jornalistas normalmente chegam a uma ideia:

1. A partir de uma divulgação pública de dados;
2. A partir do acesso a um conjunto de dados exclusivo;
3. Replicando ou adaptando uma matéria anterior baseada em dados;
4. Dando seguimento a uma notícia;
5. A partir de uma pergunta;
6. A partir de uma denúncia; ou
7. A partir da exploração.

Abordagens que começam fora dos dados (perguntas, denúncias e acompanhamento de notícias) são mais difíceis. Nessas situações, é provável que você precise adaptar a ideia em resposta aos dados que realmente existem (se é que existem), e ao que esses dados de facto medem.

Em Como fazer brainstorming de ideias de matérias sobre dados da COVID-19, descrevo algumas técnicas usadas para enfrentar esses desafios. Técnicas mais investigativas são exploradas em Como usar as ‘4 etapas da curiosidade’ como estrutura para investigações e Empatia como ferramenta investigativa: como mapear sistemas para gerar ideias de matérias.

Abordagens que começam com os dados envolvem um desafio diferente: identificar e escolher possíveis ângulos de matéria a partir dos dados disponíveis. Nessas abordagens, considere o seguinte:

• Que colunas os dados têm? (O que é medido)
• Que histórias poderiam ser contadas sobre cada uma dessas medidas?
• O que falta nos dados? (O que não está sendo medido)

Em Os ângulos mais usados por jornalistas para contar histórias com dados, descrevo como diferentes ângulos podem ser encontrados em diferentes medidas.

• Se houver uma coluna para datas, meses ou anos, por exemplo, então uma história pode ser contada sobre mudança;
• Se houver uma coluna para geografia ou categoria, uma história provavelmente pode ser contada sobre classificação ou variação.
• E quase todos os dados podem ser quantificados para contar uma história sobre a escala de algo.

Etapa 2: Compilar

O processo de compilação de dados pode ser rápido e simples — ou pode ser um grande projeto de jornalismo de dados por si só.

1. A compilação mais direta é quando os dados são publicados regularmente numa determinada página (ou enviados num comunicado à imprensa), e é daí que vem a ideia.
2. Você pode encontrar dados através de técnicas de pesquisa avançada
3. Alguns dados são publicados através de uma API, e você precisará escrever código para ‘puxar’ esses dados.
4. Você pode usar scraping — onde um script explora múltiplas páginas ou documentos para compilar dados destes numa planilha
5. Você pode converter dados não estruturados, como texto, em algo que possa ser analisado
6. Você pode compilar dados através de observação, inquéritos, formulários online ou crowdsourcing. O tempo e a despesa envolvidos em tal compilação significam que raramente é usado.

Esta etapa de compilação é especialmente importante não apenas porque torna o projeto de jornalismo de dados possível, mas porque você frequentemente precisará retornar a ela como parte das etapas subsequentes: os dados podem precisar ser compilados para limpar seus dados existentes, para contextualizá-los e combiná-los, e para melhor comunicar as descobertas.

Etapa 3: Limpar

Ter dados é apenas o começo. Ter confiança nas respostas que você obtém dos dados significa poder confiar na qualidade dos dados — e às vezes isso significa limpá-los.

A limpeza normalmente assume três formas: remover erros humanos; converter os dados num formato que possibilite fazer perguntas; ou garantir que as respostas obtidas sejam precisas.

Razões comuns para limpeza incluem:

• entradas duplicadas;
• entradas vazias;
• o uso de valores padrão onde nenhuma informação estava disponível;
• formatação incorreta (por exemplo, palavras em vez de números);
• entradas corrompidas ou entradas com código HTML;
• múltiplos nomes para a mesma coisa (por exemplo, BBC e B.B.C. e British Broadcasting Corporation);
• dados ausentes (por exemplo, distrito eleitoral);
• dados mistos na mesma coluna (por exemplo, categoria e subcategoria);
• ou dados na forma errada (por exemplo, trocar colunas e linhas).

Etapa 4: Contextualizar

Como qualquer fonte, os dados fornecem uma perspetiva sobre uma história e nem sempre podem ser confiáveis. Eles vêm com suas próprias histórias, vieses e objetivos.

Portanto, como qualquer fonte, você precisa fazer perguntas sobre eles: quem os recolheu, quando e com que propósito? Como foram recolhidos? (A metodologia). O que exatamente eles querem dizer com isso?

Você também pode precisar entender jargões, como códigos que representam categorias, classificações ou localizações, e terminologia especializada.

Estabelecer o contexto para um conjunto de dados pode levá-lo a compilar mais dados. Por exemplo, saber o número de crimes reportados numa cidade é interessante, mas só se torna significativo quando você coloca isso em contexto.

Esse contexto pode ser o tamanho da população, ou o número de policiais, ou os níveis de criminalidade cinco anos atrás. Pode ser o contexto de experiências ou perceções sobre o crime, taxas de condenação ou níveis de desemprego. Pode ser o contexto das definições de categorias de crimes.

Alguma literacia estatística básica é muito útil, então dedique um pouco de tempo a ler sobre o assunto. Como Mentir com Estatísticas é um clássico curto e fácil de ler na área, que é complementado pelo mais recente Como Fazer o Mundo Somar. E o livro de Ben Goldacre, Bad Science, prepara você para cobrir dados sobre saúde e ciência.

Adicione o podcast da BBC More or Less às suas subscrições para ouvi-los discutir histórias sobre dados, e confira The Tiger That Isn’t, dos apresentadores.

Etapa 5: Combinar

Boas histórias podem ser encontradas num único conjunto de dados, mas frequentemente você precisará combinar dois juntos. Afinal, dada a escolha entre uma matéria de fonte única e uma de múltiplas fontes, qual você preferiria?

Combinar dados é frequentemente parte de colocá-los em contexto: você regularmente vai querer combinar dados sobre o número de eventos em diferentes áreas com dados sobre as populações nessas áreas, por exemplo, para classificar a frequência per capita desses eventos. Dados sobre inflação terão que ser combinados com dados de gastos para ajustar valores de diferentes anos.

Às vezes você precisa combinar dados para obter a resposta a uma pergunta:

• Novos dados precisarão ser combinados com dados mais antigos para contar uma história sobre mudança.
• Dados sobre eventos precisarão ser combinados com dados populacionais para contar uma história sobre a escala por pessoa (ou por 10.000 pessoas, etc.)
• Dados sobre o desempenho de escolas ou hospitais precisarão ser combinados com dados sobre as localizações dessas instituições, para contar uma história classificando as áreas com os melhores ou piores desempenhos médios, ou simplesmente mostrar a extensão da variação.
• Para criar um mapa exploratório mostrando a distribuição de eventos, os jornalistas regularmente combinam um conjunto de dados com dados cartográficos. Exemplos incluem: Die Spur des Geldes: Prioritäten der EU-Migrationspolitik e Die Stadtflucht, enquanto Mein Viertel, eine Blase usa a mesma técnica para mostrar uma relação entre geografia e crenças.

Em cada etapa: Questionar

Questionar envolve analisar seus dados para obter respostas — mas também ocorre em cada etapa:

• Ao conceber ideias, questione seus vieses e pontos cegos. De quem são as vozes que faltam no processo de desenvolvimento da ideia? Você está obtendo ideias apenas de conjuntos de dados públicos — você também poderia considerar dados não publicados ou compilar dados você mesmo? Você está focando no setor público mas não no setor privado? Você tende a gerar tipos particulares de ângulos de matéria mais do que outros (como mudança em vez de escala)?
• Ao compilar dados, questione quão autorizada é a fonte e quão confiável é sua metodologia. Isso afetará como você comunica os resultados e que compilação, limpeza e contextualização adicionais precisam ser feitas.
• Faça perguntas sobre que contexto pode ser necessário: populações são frequentemente necessárias para colocar o número de eventos no contexto de quantos eventos houve por pessoa. O contexto demográfico permite que você faça perguntas sobre relações potenciais com privação, idade e outros fatores. Dados históricos permitem que você coloque dados recentes no contexto de anos anteriores. Dados sobre dinheiro precisarão do contexto da inflação: qual seria o equivalente hoje de determinado valor de cinco anos atrás?
• Faça perguntas sobre quais dados você está combinando: você deve usar a população inteira ou uma faixa etária específica ou outra demografia que se relacione com a matéria? Você deve usar a inflação geral ou focar num tipo particular de bens ou serviços?

Questionar pode evitar o viés de confirmação e desenvolver ceticismo.

Contexto

Como qualquer fonte, os dados fornecem uma perspetiva sobre uma história e nem sempre podem ser confiáveis. Eles vêm com suas próprias histórias, vieses e objetivos.

Portanto, como qualquer fonte, você precisa fazer perguntas sobre eles: quem os recolheu, quando e com que propósito? Como foram recolhidos? (A metodologia). O que exatamente eles querem dizer com isso?

Você também pode precisar entender jargões, como códigos que representam categorias, classificações ou localizações, e terminologia especializada.

Estabelecer o contexto para um conjunto de dados pode levá-lo a compilar mais dados. Por exemplo, saber o número de crimes reportados numa cidade é interessante, mas só se torna significativo quando você coloca isso em contexto.

Esse contexto pode ser o tamanho da população, ou o número de policiais, ou os níveis de criminalidade cinco anos atrás. Pode ser o contexto de experiências ou perceções sobre o crime, taxas de condenação ou níveis de desemprego. Pode ser o contexto das definições de categorias de crimes.

Alguma literacia estatística básica é muito útil, então dedique um pouco de tempo a ler sobre o assunto. Como Mentir com Estatísticas é um clássico curto e fácil de ler na área, que é complementado pelo mais recente Como Fazer o Mundo Somar. E o livro de Ben Goldacre, Bad Science, prepara você para cobrir dados sobre saúde e ciência.

Adicione o podcast da BBC More or Less às suas subscrições para ouvi-los discutir histórias sobre dados, e confira The Tiger That Isn’t, dos apresentadores.

Etapa 6: Comunicar

A visualização é a forma mais óbvia de comunicar uma história que você encontrou nos dados – mas não é a única. Outras incluem:

• Usar apenas narrativas textuais
• Usar estudos de caso de pessoas reais para dar vida aos dados
• Usar interatividade para ajudar o utilizador a ver como pode ser pessoalmente afetado
• Transformar dados em sons ou objetos físicos para fornecer outras formas de interagir com os dados
• Criar ferramentas que capacitem a audiência a usar os dados de forma prática

Como há muito mais a dizer sobre isso (veja o diagrama abaixo), escrevi um post separado sobre o tema. Aguardo com expectativa o vosso feedback sobre a pirâmide!

Traduzido com Claude Sonnet 4.5. Por favor, diga-me se encontrar algum erro nos comentários ou através do LinkedIn.

Too often discussion around using AI is “either/or” — an assumption that you either use AI for a task, or do it yourself. But there’s another option: do both.

“Parallel prompting“* is the term I use for this: while you perform a task manually, you also get the AI to perform the same task algorithmically.

For example, you might brainstorm ideas for a story while asking ChatGPT to do the same. Or you might look for potential leads in a company report — and upload it to NotebookLM to perform the same task. You might draft an FOI request but get Claude to draft one too, or get Copilot to rewrite the intro to a story while you attempt the same thing.

Then you compare the results.

The best of both worlds

There are a number of advantages to parallel prompting:

• You get the ‘best of both worlds’: AI can draw on a vast amount of indiscriminate information; you draw on a narrower but more selective knowledge base specific to your context
• It reinforces and expands knowledge: drafting a prompt encourages you to ‘teach’ the AI what you know, a common way to reinforce knowledge. It might also encourage you to learn more about something in order to design a better prompt.
• It forces externalisation and review: drafting a prompt also encourages you to break down a problem and consider your criteria for a successful result. Comparing AI responses with your own results encourages you to be more critical about both.
• Human bias and AI bias can both be addressed: AI is biased in different ways to humans, who have cognitive biases. Comparing results allows you to address both.
• It improves prompt design skills: the more prompts you design, the better you get — you’re skilling up, not deskilling.

Of course, parallel prompting means that you’re not necessarily saving time by getting AI to do a task for you — at least in the short term.

But it can save time in the long term.

For example, an FOI request drafted this way may lead to fewer problems further down the line. Or interview questions generated using parallel prompting might save time chasing up extra information later. You might do some data analysis at the same time as asking an AI tool to perform the same analysis — highlighting mistakes that you (or AI) tend to make, or better methods you can use in future.

In other words, it’s a learning process through being exposed to new ideas and methods — precisely the opposite of the deskilling that can result from poor use of AI.

*Parallel prompting has also been used to refer to using multiple prompts with different personas at the same time. That’s cool too, but not how I’m using the term.

The checklist — M.A.T.T.E.R. — covers six things to consider: Meaning, ‘Affect’ (emotion), Type of format, Temporality, Ethics and relevance.

“It offers a fast, structured way to assess what a meme does, why it works, and whether it actually matters,” Bösch writes, encouraging reporters “to slow down, separate feeling from meaning, assess impact and ethical risk, and make a conscious editorial decision” before embedding or covering a meme.

The MATTER checklist

Meaning is all about checking if you understand the background to the meme, who is likely to understand it, and “which parts of its meaning are coded, dogwhistled, or only visible to in-groups.”

Affect asks “what emotion does the meme trigger first? Is it earnest, ironic, cynical, or ambiguous?”

Type involves looking at the format of the meme, if AI is involved, and to what extent it’s native to a particular platform.

Temporality requires you to consider how long the meme has existed and whether its meaning has changed in that time, as well as where it is in its lifecycle (for example, is it likely to trigger widespread recognition, or boredom)

Ethical considerations include any use for propaganda purposes and agenda-setting, the involvement of vulnerable people, trauma or harassment, and the potential to cause harm.

Relevance involves relating the meme to classic values around newsworthiness, asking the question why it matters now. It might be because of who is circulating the meme, or the impact that it is having. Relevance also relates to the audience and whether it is circulating among that audience and likely to affect them.

Marcus says he has tested the approach “on a mix of older and current memes” and is inviting others to try it out. You can contact him with feedback at m@marcus-boesch.de

A desire for friction

One person worried about the loss of friction that AI represented: using AI tools increased the risk that they would complete tasks too easily, without the friction that would help them to learn.

Low friction is a recurring challenge in teaching, learning and journalism: as access to information — and each other — has become easier through search engines, mobile phones and email/social media, expectations and behaviours have changed.

This lack of friction means less time for strategic choices about what information to use, and how to source it, or who to approach, and how.

It can lead to sloppiness and impatience, and reduce opportunities for developing problem-solving skills and persistence.

Done right, however, using AI can actually reintroduce friction into a process: this is what happened when I introduced an AI diary into my teaching last year, leading to an increase in reflection and problem-solving.

Using hybrid destination-prompting can also design friction into the process.

Concern over boundaries

Another attendee was concerned about crossing the boundaries that we set for students. “How,” he argued, “can we ask them not to use AI if we use it ourselves?”

One challenge here is that it is also difficult to enforce a boundary if you don’t know when it’s been crossed. This is especially difficult if you’ve not used AI yourself and developed a feel for its output.

Another challenge is that those boundaries differ between news organisations and universities — and are constantly shifting.

Part of the role of journalism study is to critically interrogate those boundaries and the arguments around them in order to know where and when they should be enforced.

For example, through experimenting with AI we might identify that the most important boundaries are determined by risk, or truthfulness (e.g. being up to date) or bias. We might also identify good and bad practice, or where boundaries are not clearly defined.

A desire to work hard

One person enjoyed working hard on projects, and did not want AI to do that work for her.

There are two concepts tied up in this sentiment: job satisfaction, and effort.

AI can increase the risk that less effort is expended on our work. But this is just one choice. Another is to invest the effort elsewhere.

The inventions of other labour-saving devices provide useful analogies here. Once the washing machine was invented, for example, a choice opened up: we could continue to work hard to wash clothes by hand, exercising certain muscles and maintaining that skill — or we could use the time to exercise other muscles or develop other skills.

So using AI doesn’t mean you don’t work hard — you will probably just work hard on something else, such as sourcing and conducting interviews, verification and factchecking, writing and editing — or even learning AI-based skills such as prompt design.

And this is where job satisfaction plays a role.

A reflection by one of my students in the conclusion to their AI diary last year was a particularly good example of this: “After I’d become comfortable with the AI,” she wrote, “it was no longer enjoyable to use because I wasn’t mentally stimulated by my work.“

As a result of this, along other concerns, she made a conscious critical decision to use it less.

Equally, someone may find the stimulation provided by their work fades to a degree where they prefer to use AI to free up time for them to focus on more challenging parts of reporting.

A desire for expertise

“I want to be an expert” said another person. The implication here was that you can only become an expert in something if you don’t take short cuts, i.e. use AI.

This is something I’ve written about in my posts on journey and destination prompts.

Yes, AI can offer a tempting shortcut, and research suggests when it is used in this way we retain less information and have lower cognitive activity (the same applies to a lesser degree to using search engines).

But that isn’t the only way that AI can be used.

Journey prompting — asking for advice on a process, such as learning — can be used to help design a more effective strategy to becoming an expert, for example.

Role play prompts are a particular example of this: creating a mentor can help support the process of building expertise in an area.

And AI’s translation abilities — not just between languages, but between specialist vocabularies and levels of literacy — are particularly suited to getting to grips with a field where jargon and obtuse documentation can act as barriers to learning.

It is important to remember that AI is actually just an interface to information (its training material). And other interfaces — reading a book or being taught by an expert — are also ‘shortcuts’ to developing expertise (compared to, for example, practice, trial and error).

In fact, almost all learning is a trade-off between experience and efficiency.

And as part of that trade-off we need to recognise the limitations of each approach that we are using: books are helpful but not as effective as experiencing a process ourselves; experts are more interactive but we may find them hard to understand, or their experience may be limited; AI is extremely flexible, but the quality and reliability is variable and needs further checking and exploration.

Privacy

“What is it learning about me?” was the final objection and a reminder that surveillance capitalism — where you become the product — is an important consideration when using these tools.

There are ways that you can reduce the degree to which a large language model ‘learns’ from your activity, such as turning off memory and paying for pro versions of AI tools, but this is certainly an area to keep an eye on in the coming years alongside the data we provide in our search behaviour, browsers and phones.

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.

The industry has a destination habit

The idea of ‘journey’ and ‘destination’ prompts is to distinguish between prompts that develop skills and strategies (the journey), and prompts that focus on a simpler endpoint that saves time by ‘skipping to the end’ (the destination).

Using AI for research, for example, tends to involve a destination prompt that asks for an answer to a question. Summarising large or multiple documents, generating images or other content, lead identification, and translation are other examples.

In other words, the most common applications of large language models (LLMs).

These applications might appear lazy, but there are various reasons why we might use AI for these tasks:

• Empowerment: It might be the only way we can do that task (e.g. we don’t have budget for a translator or illustrator; we don’t have time to read hundreds of documents)
• Serendipity: It might spot things that we missed (e.g. lead identification alongside our own idea generation)
• Prioritisation: It might free up time for more valuable activity (e.g. drafting content frees up time for research)
• Efficiency: It might be cheaper or easier to use AI than not doing so.

Those are the opportunities — but there are also risks…

The risks of skipping to the end

There are two risks in particular to consider when using a destination prompt: that the quality of the work will be unacceptable (including but not limited to hallucination); and that automating or delegating that work to an LLM will have a negative impact on you or your colleagues, such as deskilling or reduced critical thinking.

When incorporating AI into a workflow, then, it is important to take steps to manage those risks — just as should happen when choosing to delegate similar tasks to an intern, freelancer, or junior member of the team.

Because delegating is precisely what is happening here — and as soon as you begin delegating you become not just a researcher or writer: you become a manager. [UPDATE: Ethan Mollick explores this in depth in his January 27 newsletter on management as an AI superpower]

One Microsoft study describes it as task stewardship: instead of your time being occupied only with research and writing, it now involves commissioning (i.e. designing prompts), answering questions and providing feedback, and selecting, checking and editing the results for quality and accuracy.

A further risk to consider, then, is the simple one of job satisfaction, and where you feel your efforts are most rewarding.

Enter the hybrid destination-journey prompt

Hybrid destination-journey prompting involves augmenting a basic destination prompt with some consideration of your own role in the process. That might be your skills development, critical thinking, or simply steps that might be taken to ‘manage’ the response.

Below is a simple example of a hybrid prompt, which asks a factual question (destination prompting) but adds a question focused on building verification skills (journey prompting).

Who is the head of English at Birmingham City University? Explain how I could verify that information outside of ChatGPT. 

Regardless of the response, even a very basic hybrid prompt such as this ensures that the user considers the risks involved in using AI, encouraging critical thinking, editorial independence and quality control, and reducing the risk of deskilling.

Here is another example, this time for summarising a document:

I am a journalist doing background research on a story on [topic]. Summarise the document attached. 
Highlight any points I should verify directly in the source or through additional references. 
Suggest three questions I should ask myself before relying on this summary in my work.

The importance of critical prompt design

If ‘writing an effective commissioning brief’ is a skill we need to develop in managing AI, we should also be designing a more detailed prompt that considers the limitations of the tool we are working with — one that incorporates critical prompt design techniques to manage AI’s tendencies towards sycophancy, greediness and hallucination.

Here is an example of a better designed hybrid prompt which does that:

Is it possible to identify the head of English at Birmingham City University? Explain how confident you are in the response and why. 
If the answer is uncertain or unverifiable, say so explicitly. 
Explain how I could verify that information outside of ChatGPT. 
Once you have answered those questions, do not offer to check any further websites.

That prompt adds a number of prompt design techniques:

• An open question rather than a direct order (to reduce the risk of hallucination): Is it possible to identify
• Self-criticism prompting (confidence estimation): Explain how confident you are in the response
• Uncertainty prompting: If the answer is uncertain or unverifiable, say so explicitly
• Negative prompting (to prevent greediness, or scope creep): do not offer to check any further websites

A prompt summarising a document should be similarly designed to counter-balance the gullibility bias of LLMs

Build enough knowledge to judge the responses

A growing body of research on the impact of AI usage on critical thinking suggests that AI usage can have a negative impact unless we have, or build, crucial subject knowledge and an awareness of AI’s flaws.

One (frequently misquoted) Microsoft study identifies two key factors that shape critical thinking (defined as including “analysis, synthesis, and evaluation”): a person’s confidence in AI, and their confidence in performing the task that they are asking the AI to perform.

If a person has high confidence in their own ability to perform a task, and low confidence in AI’s ability, they are likely to use more critical thinking in the task

But if a person has low confidence in their own ability to perform a task, and high confidence in AI, they are likely to use less critical thinking.

The takeaway is clear: we should ideally build knowledge and experience of a task ourself before asking AI to perform it.

If that is not possible, we should be especially rigorous in finding out and understanding what qualities are important in assessing the results of such tasks.

Either way, we should also be conscious that AI suffers from a number of flaws when performing tasks which will need to be checked for.

There are no shortcuts

For example, if we were asking an LLM to draft a news article we should really have experience of writing enough news articles ourselves to be confident of judging the results.

If we don’t then we have the responsibility to at least understand certain things to judge it effectively: the principles of news article structure, grammar and spelling, law and ethics (e.g. accuracy, objectivity), and the style requirements of the target publication are just some that we might list. We might also seek help from someone who does have the experience we lack.

If we don’t have experience or knowledge in an area, use of AI is particularly high risk because we have no way of judging whether it has produced something usable or not, regardless of how impressive it seems (if lawyers repeatedly fall for it, so will you).

Even with experience and knowledge, you will also need to bear in mind AI’s key weaknesses:

• AI’s verbosity leads to a tendency to overwrite;
• Hallucination creates issues around accuracy;
• The training of LLMs on a range of material outside of your context means they are likely to suffer from flaws in style that will need significant adaptation for your country, industry, and target publication.

An MIT study adds another consideration: if we use a muscle more, it becomes stronger; but if we use it less, it becomes weaker.

So, the more we spend our time on new ‘muscles’ like prompt design, interviewing and verification, the stronger those ‘muscles’ should become — but using AI costs us the opportunity to develop other muscles, whether that’s research, summarisation, or writing.

That balance between an opportunity and a cost is something to consider carefully, and a risk to manage too.

Q: What skills do you think a journalist must absolutely have when working with data?

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.

The next two technical skills are probably visualisation and pivot tables.

Pivot tables allow you to quickly turn detailed data into a summary table. That table might allow you to rank the worst or best areas or categories, and it can allow you to look at how those have changed over time. Those summary tables are also exactly what you need to create a bar, pie or line chart.

There’s a lot of talk about coding but in practice I would say this is much less important than those core spreadsheet skills, and probably only used in no more than 5% of data-driven stories.

It becomes more important once you start to move into stories that require scraping, or dealing with large datasets, or advance planning or time-saving (i.e. writing code that will analyse the data when it’s released, or analysis that you are doing regularly).

5. Are governments and institutions transparent enough in regard to open data?

I’m always going to say governments and institutions can be doing a lot more in relation to open data and transparency. A big problem is that, in general, the choice of what to release lies with the organisation and so what is released can potentially be a distraction from what is important. As journalists we need to be aware of that.

To some extent it’s not about organisations being more transparent — it’s about government and regulators passing laws and enforcing them to ensure that the most important data is required to be published.

Q: As you emphasised in the Data Journalism Handbook, storytelling plays a crucial role in the analysis of datasets. What do you see as the most critical step in transforming data into a “story”?

I always advise my students and trainees to look at the list of columns in a dataset and identify the ones that they think are most important or newsworthy — and then think about that column in terms of the 7 angles of data journalism: what stories can they tell about things getting better or worse? About who/what is ranked worst or best? Or how well your audience’s area or country is performing? Or the scale of a problem? 

If you feel there are a number of stories to tell, you might choose to do something more in depth with each of those stories forming one chapter in a feature that offers to ‘explore’ the issue or present a summary ‘in numbers’.

People often look for relationships but this is generally a mistake, especially for beginners: it is rare that we can prove a causal relationship, and trying to can result in an overcomplicated and unsatisfying story. Relationship stories require more time and experience. 

Q: Data journalism has become increasingly important not only for professional journalists but also for activists and citizen reporters. What kind of future do you think this widespread adoption promises?

I’ve always emphasised that data is just another source, and should be treated with scepticism. Its adoption outside journalism is probably already making it clearer that ‘data’ doesn’t necessarily mean ‘fact’, and should encourage us to always ask questions of data just as we would question a human source: what are the motivations of those who collect it? How was it collected? How are key concepts defined or measured?

Classification is often where people either jump to the wrong conclusion or misrepresent what data is actually showing. One thinktank, for example, misrepresented people charged with crimes as people who had committed those crimes. Other reports have confused irregular migrants with illegal migrants, or made calculations based on a wrong or out of date population estimate.

We have also seen campaigners deciding to (mis)classify anyone described as having dark skin in crime reports as an ‘immigrant’, or failing to distinguish between crimes and criminals (one person can commit multiple crimes). 

Confirmation bias and access to almost unlimited information means it is very easy to find something that supports your position, so “cherry picking” data is widespread, with many people unaware that this is what they are doing.

We need to train people early to develop a habit of challenging their own assumptions — and as journalists we need to have the skills to check the claims made based on any dataset. 

Q: In your view, what has been the most significant transformation in digital journalism in recent years? 

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. 

Q: How do you evaluate its main differences from traditional journalism?

I think there’s very little difference now between digital journalism and “traditional” journalism. Every journalist is a digital journalist now: you would struggle to find a reporter who only writes printed or broadcast stories, and that’s been the case for many years now. Multiplatform reporting is now the norm. 

In terms of how modern reporting differs from pre-internet reporting, there’s a much stronger sense of the audience. Audience feedback was very basic before the internet. Now we have a very clear picture of what stories people read or watch or listen to, for how long, and what they do with those stories. 

Modern journalism also has lost significant control over how it reaches an audience, with that control now held by search engines, social media, and AI platforms. Because the business models and cultures of companies like Google, Meta, OpenAI now play a major role in shaping what stories audiences are likely to see, that has in turn shaped the business models and cultures of news organisations. 

So journalists must now consider the extent to which their stories fit into those infrastructures and the extent to which they are optimised for search or for social. But there’s also been a growth in forms that have more direct relationships with audiences, such as email newsletters.

Investigative journalism is much easier for modern reporters than it used to be in the analogue era, as journalists are able to gather and analyse more information, faster, and with more power.

Data journalism and OSINT are two new forms of journalism that embody this, and they also embody the much wider range of formats and genres involved in modern journalism.

Podcasts and ‘scrollytelling’, for example, are just two examples of how modern journalism has moved away from formulaic reporting towards more in-depth narrative journalism.

And interactivity has opened up all sorts of new ways of telling stories, too, not only in personalisation, maps, calculators and games — but also in the way that audiences can play an active role in newsgathering, from providing user generated content (UGC) and tip-offs to crowdsourcing. 

Q: From the time you founded the Online Journalism Blog until today, how do you see the trajectory of digital reporting?

Aside from the changes listed above, we’ve seen journalism go through a number of phases. One of the most fundamental – which is often overlooked because of all the changes that followed – was the hyperlinked nature of the web, which made us think about how our reporting connected with the rest of that network, both informationally (linking to sources, and to further information) and socially (connecting with audiences). 

Blogging was another early major change which introduced additional competition that forced journalists and publishers to up their game. It was no longer enough to merely report what people were saying, because our sources now had a direct channel to our audiences. It’s easy to forget how stenographic most journalism was before.

Journalists then had to adapt to new forms of information including data and UGC, so we’ve seen Computer Assisted Reporting become data journalism, an expansion of factchecking, and the emergence of OSINT. Information overload created both supply and demand for curation as a skillset.

Mobile phones added to this, and transformed newsgathering, putting a piece of kit into every reporter’s hands that meant writers had to learn how to film and record clear audio. And because that kit was also in the hands of audiences, and as bandwidth has increased to facilitate it, and algorithms tweaked to prioritise it, the language of news has become less textual and much more visual.

There’s been a tragedy of the commons as initial optimism about the internet has come up against increasing pollution and weaponisation of a public sphere built on algorithms that have learned the most basic human emotional triggers for content that keeps us on the page are hate and anger.

We may well look back at this time as a digital cold war, with dozens of countries funding armies of trolls and sock puppets to destabilise adversaries, and most people completely unaware or unbelieving that the content they were sharing was not authentic. 

But it’s easy to focus on what we’ve lost and forget what we have gained. The voiceless have more of a voice than they did three decades ago; there are more tools to hold power to account, and they are cheaper and more accessible than ever. We have more creative ways to tell stories than we ever did, and as a result the stories are richer and more diverse than they ever were. 

Q: What are your thoughts on the use of artificial intelligence and automation tools in journalism? How do these developments affect the ethical boundaries of the profession?

Automation is just one application of AI, and journalism has used automation for over two decades now, so the ethical challenges are not new. It has always been important to ensure that the outputs of automated steps in a workflow are regularly checked and edited (what is now called ‘Human In The Loop’), and that any algorithm itself is regularly checked and updated. 

Those checks consider the key ethical consideration of accuracy, and ensure another, editorial independence. But if the subject matter or material is likely to raise other issues, major automation is normally not used.

For example, automation might be used to generate stories for earthquake alerts or financial results or football matches where the source is reliable, factual and authoritative, and there is no need for ‘balance’. But in less clear-cut contexts, it might be used only to alert journalists in much the same way that a press release, email alert or newswire. For example Dataminr uses machine learning to classify tweets as newsworthy and alert journalists to those. 

What is different about automation with genAI is that it is a probabilistic tool. So whereas previous automation technologies made mistakes in a systematic way, in which the same input will always lead to the same output, generative AI does not: the same input can lead to different outputs; it’s a roll of the dice each time. That means it requires closer monitoring of both input and output, especially in relation to known weaknesses of AI such as accuracy and bias.

Another major difference is our reliance on third party algorithms. When we automate with someone else’s LLMs (OpenAI, Google, Anthropic etc) we have no control over those algorithms, and may not know if they are changed while we are using it – especially given that they are essentially being constantly trained on the inputs of a world of users.

For that reason it is important to ‘train’ the model as much as possible, whether that is through building your own LLM, or creating a custom GPT, or through prompt design techniques. All of those are examples of asserting some editorial control.

Editorial independence is a complex issue here. Like most ethical considerations, it has to be considered alongside other ethical issues. For example, news organisations lose editorial independence by allowing it to be indexed by Google (allowing the importance of our stories to be decided by their algorithms), but the ethical considerations of accessibility and informing audiences (and having large enough audiences to be financially sustainable) win out over that.

We can see similar trade-offs with AI: some reporting would not be possible without it, and AI can be a useful tool in improving how well we consider ethical issues (it can aid accuracy and bias, even though those are also weaknesses in general).

The second part of this FAQ, focusing on data journalism and open data, is published tomorrow.