Paul teaches data journalism at Birmingham City University and is the author of a number of books and book chapters about online journalism and the internet, including the Online Journalism Handbook, Mobile-First Journalism, Finding Stories in Spreadsheets, Data Journalism Heist and Scraping for Journalists.
From 2010-2015 he was a Visiting Professor in Online Journalism at City University London and from 2009-2014 he ran Help Me Investigate, an award-winning platform for collaborative investigative journalism. Since 2015 he has worked with the BBC England and BBC Shared Data Units based in Birmingham, UK. He also advises and delivers training to a number of media organisations.
There are three broad paths in ethics. Image by pfly CC BY-SA 2.0
Many people — including me — are quite uncomfortable with generative AI. Most of this discomfort can be traced to the various ethical challenges that AI raises. But an understanding of the different schools of ethics can help us both to better address those challenges and what to do about them.
Three different ethical approaches
The first thing to say about the ethics of AI is that there is no single ‘ethics’. When we engage with ethical issues there are typically at least three different systems that might be in play:
Journalists are no strangers to disagreement: the job regularly involves reporting on conflicts, putting one party’s point of view to another, or engaging with audience challenges around bias and veracity.
The Bureau of Investigative Journalism’s Big Tech Reporter Niamh McIntyre has been working with data for eight years — but it all stemmed from an “arbitrary choice” at university. She spoke to MA Data Journalism student Leyla Reynoldsabout how she got started in the field, why you don’t need to be a maths whizz to excel, and navigating the choppy waters of the newsroom.
Starting out on any new path can be daunting, but in the minutes before my phone call with Niamh McIntyre, I’m acutely aware that upping sticks to Birmingham and training in data journalism at the grand old age of 29 is nothing less than a tremendous luxury.
Strong factual storytelling relies on good idea development. In this video, part of a series of video posts made for students on the MA in Data Journalism at Birmingham City University, I explain how to generate good ideas by avoiding common mistakes, applying professional techniques and considering your audience.
In the latest in a series of posts on using generative AI, I look at how tools such as ChatGPT and Claude.ai can help help identify potential bias and check story drafts against relevant guidelines.
We are all biased — it’s human nature. It’s the reason stories are edited; it’s the reason that guidelines require journalists to stick to the facts, to be objective, and to seek a right of reply. But as the Columbia Journalism Review noted two decades ago: “Ask ten journalists what objectivity means and you’ll get ten different answers.”
Generative AI is notoriously biased itself — but it has also been trained on more material on bias than any human likely has. So, unlike a biased human, when you explicitly ask it to identify bias in your own reporting, it can perform surprisingly well.
It can also be very effective in helping us consider how relevant guidelines might be applied to our reporting — a checkpoint in our reporting that should be just as baked-in as the right of reply.
In this post I’ll go through some template prompts and tips on each. First, a recap of the rules of thumb I introduced in the previous post.
One of the techniques that can be used to come up with more creative story ideas is the SCAMPER method.
SCAMPER is a mnemonic for seven different actions that can be applied to ideas in order to improve those ideas or generate more interesting alternatives. It is a technique adapted from design and engineering circles — but with just a little thought it can be applied to journalism too.
In the fifth of a series of posts from a workshop at the Centre for Investigative Journalism Summer School, I look at using generative AI tools such as ChatGPT and Google Gemini to help with reviewing your work to identify ways it can be improved, from technical tweaks and tightening your writing to identifying jargon.
Having an editor makes you a better writer. At a basic level, an editor is able to look at your work with fresh eyes and without emotional attachment: they will not be reluctant to cut material just because it involved a lot of work, for example.
An editor should also be able to draw on more experience and knowledge — identifying mistakes and clarifying anything that isn’t clear.
But there are good editors, and there are bad editors. There are lazy editors who don’t care about what you’re trying to achieve, and there are editors with great empathy and attention to detail. There are editors who make you a better writer, and those who don’t.
Generative AI can be a bad editor. Ensuring it isn’t requires careful prompting and a focus on ensuring that it’s not just the content that improves, but you as a writer.
One of the most common reasons a journalist might need to learn to code is scraping: compiling information from across multiple webpages, or from one page across a period of time.
But scraping is tricky: it requires time learning some coding basics, and then further time learning how to tackle the particular problems that a specific scraping task involves. If the scraping challenge is anything but simple, you will need help to overcome trickier obstacles.
Large language models (LLMs) like ChatGPT are especially good at providing this help because writing code is a language challenge, and material about coding makes up a significant amount of the material that these models have been trained on.
This can make a big difference in learning to code: in the first year that I incorporated ChatGPT into my data journalism Masters at Birmingham City University I noticed that students were able to write more advanced scrapers earlier than previously — and also that students were less likely to abandon their attempts at coding.
You can also start scraping pretty quickly with the right prompts (Google Colab allows you to run Python code within Google Drive). Here are some tips on how to do so…
Spreadsheet analysis is part of the research phase of a story
Generative AI tools like ChatGPT and Gemini can be a big help when dealing with data in spreadsheets. In this third of a series of posts from a workshop at the Centre for Investigative Journalism Summer School (the first part covered idea generation; the second research), I outline tips and techniques for using those tools to help with spreadsheet formulae and reshaping data.
Whether you come across data as part of story research, or compile data yourself, chances are that at some point you will need to write a formula to ask questions of that data, or make it possible to ask questions (such as creating a column which extracts data from another).
If you find yourself coming up against the limits of your spreadsheet knowledge, then genAI tools can be useful both in breaking through those — while expanding your knowledge of functions and formula writing.
Writing spreadsheet formulae with ChatGPT or other genAI tools
Generative AI can be used at all points in the journalism process: this post focuses on the research stage
In the second of a series of posts from a workshop at the Centre for Investigative Journalism Summer School (read the first part on idea generation here), I look at using generative AI tools such as ChatGPT and Google Gemini to improve sourcing and story research.
Research is arguably the second-highest risk area (after content generation) for using generative AI within journalism. The most obvious reason for this is AI’s ability to make things up (“hallucinate“) — but there are other reasons too.
Online Journalism Blog 