Tag Archives: artificial intelligence

Definite articles: AI

Welcome to ‘Definite articles’, our pick of recent editing-related internet content, most of which are definitely articles. This time, our theme is the impact of artificial intelligence (AI) on editing and proofreading. It’s a hot topic of conversation among editorial professionals, which is why some of the links in this article were sourced from a CIEP forum thread about ChatGPT. Thank you to the CIEP members who shared them.

Because nothing related to discussions about AI can be guaranteed a long shelf life, you should know that this edition of ‘Definite articles’ was put together at the beginning of June 2023. It covers:

What’s been happening?
What can AI actually do?
How can editorial professionals move forward with AI?

What’s been happening?

On 30 November 2022, the AI chatbot ChatGPT was released by OpenAI. Since then, people who work with words, who include editors, proofreaders and writers, have had the unnerving feeling that the fundamentals of what they do might change, at least in some areas. If you haven’t been keeping a close eye on events, Forbes has written a short history of ChatGPT and two professors have summarised some of the implications of ChatGPT in usefully easy-to-understand terms. You can get an overview of Microsoft’s Copilot, an AI assistance feature being launched this summer, from CNN and Microsoft itself.

As well as the obvious nervousness about whether AI would replace various categories of worker, concerns were quickly raised about the effects of AI on assessing student work and what AI might mean for copyright.

By late spring 2023, loud noises were being made about regulation of AI. As lawmakers in Europe worked on an AI Act, workers in the UK reported that they would like to see the regulation of generative AI technologies.

It’s a subject that’s currently being written and thought about on a daily, if not hourly, basis. But, in practice, and at this point in time, what can AI actually do?

What can AI actually do?

If you didn’t catch Harriet Power’s CIEP blog, ‘ChatGPT versus a human editor’, it’s an enlightening and entertaining read that went down well with our social media followers on LinkedIn, Facebook and Twitter. Harriet instructed ChatGPT to take a proofreading test, write a blog post, and edit some fiction and a set of references. In the proofreading and editing tasks, it did ‘pretty well’ and was impressive in simplifying a fiction passage while keeping its main points. It also wrote a serviceable blog draft.

The two main problems Harriet noticed in the technology were a distinct lack of sparkle in ChatGPT’s writing and editing, and its ‘tendency to “hallucinate”: it’s very good at making stuff up with complete confidence’. (This tendency was also written about by Susanne Dunlap for Jane Friedman’s website, in an article called ‘Using ChatGPT for book research? Take exceeding care’.) Weighing up her test run, Harriet concluded:

ChatGPT apparently struggles to remain coherent when responding to much longer pieces of text (like whole books). It isn’t always factually accurate: you can’t entirely trust anything it’s saying. I can’t imagine how it’d make a good development editor, or how it’d handle raising complex, sensitive author queries. It can’t track changes well. It can’t think like a human, even when it can convincingly sound like one.

However, Harriet added the caveat that in her view it may be ‘years or even months’ before ChatGPT might be able to start competing with human editors. So, how should we respond to that?

How can editorial professionals move forward with AI?

Perhaps there’s no choice but to look at the possible upsides of the AI debate. Anne McCarthy for the New York Book Forum starts us off in ‘The potential impact of AI on editing and proofreading’ by reminding us that lightbulbs and the ‘horseless carriage’ inspired dire predictions in their day. She concludes: ‘Books always have (and always will) require a human touch: it’s what draws us readers to them.’

Amanda Goldrick-Jones, in an article for the Editors Toronto blog called ‘ChatGPT and the role of editors’, offers some wise and hopeful advice: there’s a point at which we, as editorial professionals, have to trust ourselves.

If anyone is well-positioned to explore and critique the possibilities and challenges of AI-generated writing, it’s an editor … So, as with other communication technologies, editors must self-educate about its affordances, propose clear ethical boundaries, and critically engage with its limitations. It’s a tool, not our robot overlord.

Part of this consideration and engagement is understanding AI’s risks, and Michelle Garrett lays these out very effectively in a blog post from March, ‘The realities of using ChatGPT to write for you – what to consider when it comes to legalities, reputation, search and originality’.

Moving one step further, a Q&A with writer Elisa Lorello on Jane Friedman’s website talks about actively using ChatGPT to become ‘creatively fertile’. Lorello testifies that when she started using the technology in earnest, ‘It’s like I suddenly gained an edge in productivity, organization, and creativity’.

And finally, Alex Hern in The Guardian described what happened when he spent a week using ChatGPT to enhance his leisure activities. If you’re not ready to use AI at work, perhaps you could at least get a couple of recipes out of it.

With thanks to the users of the CIEP’s forums for the links they shared in recent discussions.

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.
Find out more about:

Photo credits: robot hand by Tara Winstead on Pexels; OpenAI screen by Jonathan Kemper on Unsplash.

Posted by Sue McLoughlin, blog assistant.

The views expressed here do not necessarily reflect those of the CIEP.

ChatGPT versus a human editor

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Can ChatGPT hold its own against a human editor? Can it tackle typical editorial tasks? Harriet Power puts it through its paces.

ChatGPT describes itself as an ‘AI language model’: it’s essentially a clever chatbot that you can have human-like conversations with. It’s been trained on enormous amounts of text data (such as websites and books) to learn how language works. ChatGPT tells me that when someone gives it a question or prompt, it ‘breaks down the text into individual words and analyses them to understand the user’s intent. It then generates a response by predicting the most likely next words based on its training data’.

It turns out that ChatGPT is quite good at a variety of things, from writing marketing copy to summarising policy documents to creating computer code, and there’s been a fair bit of noise online about how it’s going to take over all our office-based jobs. But could it take over editing and proofreading? Could it start writing posts for the CIEP blog? I decided to put ChatGPT-4 through its paces with some typical editorial tasks.

Take a proofreading test

How does ChatGPT fare with the CIEP’s proofreading test? This test is available here; it’s a 270-word piece of text with 20 ‘common’ errors. I pasted the text into ChatGPT with the prompt ‘Please point out the proofreading errors in this text’ (it’s hard to let go of ingrained politeness, even when talking to a machine).[1]

If we ignore the 3 formatting errors that didn’t copy across, ChatGPT caught 15 out of 17 errors. It did well at spotting spelling mistakes (such as ‘peaking’ rather than ‘peeking’) and repeated words (‘There had certainly had been one or two eccentric characters’). It spotted that Anne’s cup of tea had morphed into a cup of coffee three paragraphs later, which according to my programming boyfriend is an impressive catch to make.

It missed a hyphen that should have been an en dash, and didn’t change ‘Jones’ geraniums’ to ‘Jones’s geraniums’, although that’s arguably just a style choice (as the test itself acknowledges). Another thing it didn’t do was query how Ann spells her name: it assumed ‘Anne’ was right (probably because it was spelled this way where it first occurs in the text).

So perhaps ChatGPT’s main failing here was to not question the text in a way a human would: to ask about style or spelling preferences. Plus of course it can’t see formatting errors, or track changes in a useful way, but both of those issues might be solved once Microsoft’s Copilot is launched sometime ‘in the months ahead’. This will basically integrate ChatGPT into Microsoft programs. At that point it might be perfectly possible for Copilot to proofread text in Word and mark up corrections using Track Changes and use a style guide for reference. Scary, right?

Write a blog post

How about writing posts for the CIEP blog? ChatGPT is certainly much quicker at this than us slow and plodding humans, taking under a minute to write a post. The results are quite bland and generic, but are also pretty serviceable. For example, here’s the start of a blog post about how to start out as a freelance copyeditor:

Embarking on a freelance copyediting career can be both an exciting and daunting prospect. With the right preparation and know-how, however, you can set yourself up for success in this rewarding field. In this blog post, we’ll explore the essential training and resources you’ll need to get started, as well as tips for finding your first copyediting jobs.

Part A: Training for Success

Understand the basics

Before diving into the world of freelance copyediting, it’s essential to familiarize yourself with the basic principles of the English language. This includes understanding grammar, punctuation, syntax, and spelling rules. There are numerous online resources and books available to help you strengthen your foundation in these areas.

Formal training

While it’s not strictly necessary to have formal training to become a freelance copyeditor, it can help you refine your skills and lend credibility to your services. Many organizations offer courses and certifications to help aspiring copyeditors learn the ropes. Some recommended courses and programs include:

- The Chartered Institute of Editing and Proofreading (CIEP): CIEP offers a range of courses, from basic proofreading and copyediting to more advanced courses on specific topics. Their Introduction to Copyediting and Proofreading courses are great starting points.
- The Publishing Training Centre (PTC): The PTC provides a comprehensive copyediting course called ‘Basic Editing by Distance Learning’ that covers grammar, punctuation, and syntax, as well as editing techniques and best practices.
- The Editorial Freelancers Association (EFA): EFA’s ‘Copyediting: Beginning to Intermediate’ course is designed to help you build skills and knowledge in various areas of freelance editing, including copyediting, with a focus on practical applications and exercises.

With a little tweaking that could turn into a passable (if not sparkling) blog post. The main problem with it is that no courses exist with the exact titles ‘Basic Editing by Distance Learning’ or ‘Copyediting: Beginning to Intermediate’. This illustrates one of ChatGPT’s main limitations, which is its tendency to ‘hallucinate’: it’s very good at making stuff up with complete confidence, rather than simply admitting when it doesn’t know something.

Edit fiction

I amused myself with writing some terrible fiction, then asked ChatGPT to improve it. Here’s what we started with:

Outside, the trees swayed ominously in the howling wind while dark, menacing clouds raced overheard. Rachel was gazing wistfully out of the window longing to be out in the paddock with her horse Ophelia irregardless of the fetid weather, when there was a resoundingly loud crash and a blood curdling screaming from above and she jumped ten feet in the air. Panicked thoughts flew threw her mind as she wrenched open the kitchen door, which persistently stuck as it grazed the uneven flagstones, and pelted downstairs where she found her brother tangled on the floor of the hallway in the rich velveteen curtains with the bust of Artemis, the goddess of love, shattered and lying in forlorn pieces around his beautiful golden-flecked hair.

“Jasper!” whispered Rachael frantically. “What happened!”

She roughly shaked him until he groaned and groggily opened one eye, gazing not at her but staring inwards at the visage that had shaken him to his very core.

“Rachel”, he moaned. “I saw him.”

“Saw who? Who did you see?” she exclaimed fearfully.

Asking ChatGPT to ‘line edit’ this led to a simple copyedit, where it corrected things like ‘overheard’ to ‘overhead’, ‘fetid weather’ to ‘foul weather’, ‘Artemis’ to ‘Aphrodite’ and so on. I then asked ChatGPT to ‘simplify the text a little’, and questioned whether the fact that Rachel heard the crash from upstairs but then ran downstairs is inconsistent. Here’s ChatGPT’s reply:

Yes, it is inconsistent. The story indicates that the crash and screams come from above, suggesting that Rachel should run upstairs instead of downstairs. Here’s the revised text to fix the inconsistency:

Outside, the trees swayed in the strong wind as dark clouds raced overhead. Rachel gazed out of the window, wishing she could be in the paddock with her horse, Ophelia, despite the poor weather. Suddenly, a loud crash and a scream from above startled her. Panicked, she opened the kitchen door, which stuck as it scraped the uneven flagstones, and rushed upstairs. She found her brother on the floor, entangled in the curtains, with the broken bust of Aphrodite scattered around his golden-flecked hair.

“Jasper!” whispered Rachel anxiously. “What happened?”

She gently shook him until he groaned and opened one eye, not looking at her but appearing deeply shaken.

“Rachel,” he murmured. “I saw him.”

“Saw who? Who did you see?” she asked nervously.

This clearly isn’t prize-winning fiction, but I was impressed that ChatGPT could take my original text and rewrite it while still keeping the essential bits of the narrative, and present them in a coherent way. It’s an example of how powerful ChatGPT can potentially be when it comes to simplifying text.

Edit references

I took a list of references, introduced a bunch of mistakes, and then asked ChatGPT to edit them so they were consistent.

ChatGPT did pretty well. It made corrections like replacing ‘and’ with ‘&’, moving the year to the correct place, changing the volume and issue number from ‘18:2’ to ‘18(2)’, and deleting the word ‘pages’ before the page range. The thing that impressed me was when I gave it a reference where I’d deleted one of the author’s initials, and ChatGPT filled them in correctly. It managed to do that correctly a couple more times, so it didn’t feel like a fluke. (Though if ChatGPT is simply making educated guesses about how to fill in the blanks then this is far from infallible.)

If ChatGPT could track changes (and my guess is that it will be able to do this in Microsoft’s Copilot) then I’d happily use it to help me edit references.

Can ChatGPT replace me?

ChatGPT is remarkable, in that it can edit and write text and you can hold a semi-intelligent conversation with it when its basic function is simply to guess what the next word should be.

As things stand, there are some stumbling blocks when it comes to using it as an editor or proofreader: ChatGPT apparently struggles to remain coherent when responding to much longer pieces of text (like whole books). It isn’t always factually accurate: you can’t entirely trust anything it’s saying. I can’t imagine how it’d make a good development editor, or how it’d handle raising complex, sensitive author queries. It can’t track changes well. It can’t think like a human, even when it can convincingly sound like one.

So I don’t think that it currently competes with a human proofreader or editor (or blog writer), but I do wonder how many years or even months it might be before it can. Things are moving at speed: in the time it’s taken to write this blog post, ChatGPT has already announced two upgrades (one from ChatGPT-3.5 to ChatGPT-4, and one to launch plugins). And when Microsoft’s Copilot launches sometime in the coming months, that’ll be another giant leap forwards.

John Naughton, professor of the public understanding of technology at the Open University, has described ChatGPT like this: just as Google became a prosthesis for memory, so will ChatGPT (or similar AI) become ‘a prosthesis for something that many people find very difficult to do: writing competent prose’. I’m certain that it’s going to have a seismic influence on an industry that helps people to write and publish competent prose; the question is just how quickly, and what editors and proofreaders can or should do about it.

[1] When I asked ChatGPT later on, ‘Do you appreciate it if I start my requests with the word “Please”?’ its answer was: ‘As an AI language model, I do not have the ability to feel emotions. However, using “please” in your requests is a courteous and polite way to ask for something, and it can help make the conversation more pleasant and respectful.’

About Harriet Power

Harriet Power develops and copyedits nonfiction books and educational materials. She is a commissioning editor for the CIEP information team, and a Professional Member of the CIEP.

About the CIEP

Photo credits: Motherboard by Pixabay on Pexels; Glasses and smartphone by Mateus Bertelli on Pexels; ChatGPT screen by Jonathan Kemper on Unsplash.

Posted by Sue McLoughlin, blog assistant.

The views expressed here do not necessarily reflect those of the CIEP.

Editors don’t just spot typos: Breaking down the editing stereotypes

Stereotype 1: Editors just spot typos

Even a little research reveals that this is not true. Scan the list of courses offered by the CIEP. Flick through the 12-page CIEP syllabus for the basic editorial test. The word ‘typo’ does not appear but the phrases ‘professional practice’ and ‘editorial knowledge and judgement’ do. The CIEP’s members are described on its homepage as ‘the people who work to make text accurate, clear and fit for purpose’. That is a broad description. Clearly, there is far more to being an editorial professional than just ‘correcting mistakes’.

Stereotype 2: Editors are the grammar police

Editors and proofreaders may suggest many types of amendments, and some of these suggestions may involve correcting grammar. Good editors and proofreaders will do so respectfully and sensitively. We don’t make judgements about the writer’s education or background. We don’t set out to destroy the writer’s self-confidence or impose our own style of writing on theirs. We won’t force the writer to make the changes we’ve marked up. They are just suggestions that we believe, in our professional capacity, will make the text more effective in achieving its purpose. The writer isn’t obliged to accept them (unless they have been commissioned to write to a specific brief).

We appreciate that seeing a screen of red Track Changes can be intimidating. We know that it can be dispiriting to be told that that long-incubated text is not quite ready for publication. But we are on the writer’s side. It should be more a partnership than a hierarchical relationship, in which we respect the writer’s vision and the writer respects our expertise.

Stereotype 3: Editors are too expensive

‘Expensive’ is a relative term. A good edit or proofread is an investment but budgets are often tight. Several hundred (or thousand) pounds is a lot of money to find, even for established publishers – in some cases, the rates they offer editors and proofreaders have actually reduced over the years.

A self-published author once told me that they’d had the budget to commission either an editor or a cover designer and had opted for the cover designer, believing that marketing was more of a priority. After all, when a book catches your eye, you’re likely to buy it before you read it. But reviews on sites such as Goodreads and Amazon, and old-fashioned word-of-mouth recommendations, also generate sales. When a reading experience is spoilt by inconsistencies, errors and impenetrable prose, those positive reviews and therefore those additional sales will not materialise.

If a client baulks at my fees, that’s their prerogative, just as it’s my prerogative to turn down a job that doesn’t meet my minimum hourly rate. Editorial professionals are running a business and need to pay the bills. And my quote for ‘doing the work’ includes not only the time taken to do the work itself but also 25 years of editing experience, both in-house at publishers and as a freelancer. Factors other than long service may also be significant. For example, those who became editors after a successful career in another field may apply the knowledge from their previous roles and qualifications to provide a specialist service, such as for legal or medical texts. Clients are paying for that knowledge, just as they would for the services of a plumber or solicitor.

Stereotype 4: Editors have been replaced by AI anyway

Artificial intelligence (AI) seems to be everywhere these days. Can computers do what editors do? Well, some editorial tasks can be performed by software. Microsoft Word has an ‘Editor’ function that suggests ‘refinements’ to aid such aspects as ‘clarity’, ‘conciseness’ and ‘inclusiveness’. The popular app Grammarly promises ‘bold, clear, mistake-free writing’. And editors themselves use a variety of tools to help them work efficiently and accurately. Few of us would contemplate copyediting without running the trusty PerfectIt or our favourite macros.

But extracting meaning from text requires not only an in-depth knowledge of the ‘rules’ of language and punctuation but also an ability to put ourselves in the heads of readers to identify what could be clearer, what could be missing, or what could be cut. We’re not merely correcting grammar and typos – we are interacting with the text, raising queries where we believe it could be made more effective. Our checks may involve formatting and presentation – for example, checking that a page layout is balanced – or they may be to do with the content and the way the argument is expressed. None of these aspects have yet, to my knowledge, been fully grasped by a computer.

Again, our personal experiences bring a very human dimension to the act of editing. Our thought processes have quirks and tangents that are difficult to program. We look at the big picture, as well as the details, and there are subtleties in language and meaning that cannot quite be quantified by a machine. We use editorial judgement to get that balance right.

In any case, as a writer, I’d much prefer to engage with a real person with real opinions. Real people will be the readers of my published work, after all.

But don’t just take my word for it. Download this focus paper, ‘Imagine … an editor’, by the CIEP’s honorary president, David Crystal, to read his inimitable take on the importance of editorial professionals. His argument is far more eloquent than mine. Perhaps I need an editor!

About Julia Sandford-Cooke

Advanced Professional Member and CIEP Information Team member Julia Sandford-Cooke of WordFire Communications has clocked up nearly 25 years in publishing. When not editing textbooks, she posts short, grumpy book reviews on her blog, Ju’s Reviews, and would like to get on with writing her novel if only work didn’t keep getting in the way.

About the CIEP

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Photo credits: header image by Pixabay, typewriter by Suzy Hazelwood, both on Pexels.

Posted by Sue McLoughlin, blog assistant.

The views expressed here do not necessarily reflect those of the CIEP.

Talking tech: Can a machine use conscious language?

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In this Talking tech column, Andy Coulson delves into the world of artificial intelligence to find out how it might be able to consider the use of conscious language or edit text in the future.

For this issue of The Edit my column is going to be a little different from normal. Usually, I try to highlight how technology can help you with the theme of the issue. This issue’s theme, conscious language, proves to be a bit of a challenge on that front. What I am going to do instead is to get the crystal ball out and do a bit of speculating about how technology might develop to help ensure more conscious language use.

Natural language processing

Natural language processing (NLP) is the term used to describe a field of computer science that covers developing computer systems to understand text and speech in a comparable way to a human. This is a branch of artificial intelligence (AI), and I will get into some more detail about that later. This enables tools like Google Translate or the digital assistants Siri or Alexa to work. This is the field from which any tools (or indeed our competitors!) will come that will be able to improve how conscious the language in a text is.

Just to simplify things (slightly) I am going to ignore speech and all the computational issues that speech recognition brings. Let us concentrate on text and look at how machines are taught to understand that and make decisions about how to respond to it. To date, a lot of the NLP development has focused more on teaching a machine to respond to some text, whereas what we are trying to think about is how a machine would understand and amend a text. Microsoft and Grammarly both use AI to help improve their editing tools, so you can be sure there are other tech companies experimenting with this.

While language is to a degree rule based, it is also full of subtleties and ambiguities. The rules allow tools like PerfectIt to work – we can describe and recognise patterns and so teach a machine to do this too. This only takes us so far, as NLP then needs to pick the text apart to find the meaning within it. It must undertake a range of tasks on the text to enable the computer to ‘understand’ it. These include:

Speech or grammatical tagging, where the computer figures out the role of each word. This would be where it would identify ‘make’ being used as a verb (make a jacket) rather than a noun (the make of jacket).
Recognising names, so it can identify a proper noun. It knows Lesley is likely to be someone’s name rather than a thing, so ‘picking Lesley up on the way’ can be interpreted in the right sense.
Resolving co-references, where it relates a pronoun to a previously named object, so it recognises that ‘she’ is ‘Kathy’ from a previous sentence. This task can also be involved with dealing with metaphors or idioms – recognising that someone who is cold may not want an extra jumper but might not be much fun to talk to.
Sentiment analysis, which is also known as opinion mining. Here the computer is attempting to recognise more hidden aspects of the text, such as whether the tone is positive or negative.

All of these, and other functions we would need in order to make judgements about how conscious the language used in a text is, do not lend themselves to rules. Rather, they rely on a knowledge of context and conventions. Acceptable language in a novel set in 1960s Alabama would be quite different from that used in a modern social sciences paper about the same city and its inhabitants, but understanding the context will frame and shape language choices.

How machines learn

So, we have realised we are not going to be able to fix this one with a clever macro. What sort of computation do we need? Step forward AI – a term that covers a number of fields that involve machines that mimic human intelligence. One of the main aspects of this that NLP uses is machine learning, a field of computing covering machines that learn a task or tasks through different approaches.

One of the best-known AI companies is Google’s DeepMind division. They have made a name for themselves by approaching AI from the perspective of learning to play games using machine learning. To understand how they have progressed in the field we need a bit of a history lesson.

In 1997 an IBM project called Deep Blue beat the then World Chess Champion, Garry Kasparov. What Deep Blue did was to search all possible moves in the game and then pick the best next move. What is different about DeepMind’s AlphaGo is that they had to follow a different approach, as the game of Go has so many more possible moves than chess. This version of AlphaGo used neural networks (a brain-like arrangement of computing elements with lots of connections between each element) to compare the best move from the current position and the likelihood of winning from that move, which gave a more efficient way of narrowing down the choice of moves. AlphaGo was trained by playing vast numbers of games of Go to improve its ability to select moves and predict its current chance of winning. Eventually, in 2016, it beat Lee Sedol, widely regarded as one of the best players of all time.

DeepMind have since developed AlphaGo further and, instead of playing against experienced players, it learns from scratch by playing against itself. It uses a technique called reinforcement learning, where the system tries to optimise a reward called a Q-value. It has been able to play and master various video games from scratch (the Atari benchmark). Here AlphaGo tries to gain positive awards (and avoid negative ones) by, for example, collecting a game’s currency or surviving for a certain amount of time. It can then use the information about what it did and what reward it received to alter its strategy and see if that improves the Q-value.

Why is this important? It shows a progression from a very controlled environment with a limited (although large) number of variables, to a more complex one (Go) and then to a more generalised one (more varied games). We are still not at the point where this could be applied to a problem (like our language one) with very few constraints, but this certainly shows a progression. The latest version, AlphaZero, has apparently taught itself chess from scratch to a world champion level in 24 hours.

This technique of using neural networks and reinforcement learning seems to me to offer the potential to create tools with a more subtle understanding of learning. One issue that can cause problems is that AI often uses huge datasets to train the systems, but using already acquired data can bring with it historical problems. Microsoft created an AI chatbot for Twitter called Tay, designed to mimic the speech patterns of a 19-year-old girl, which it did very well right up to the point it learned to be inflammatory and offensive and had to be shut down. Microsoft believe that the trolling the bot experienced taught it how to be offensive. Similarly, Amazon developed an AI system to shortlist job candidates, and this showed a distinct bias against women. Amazon tracked the problem down to an underlying bias in the training data.

Given the increasing pressure on social media companies to filter offensive content, platforms like YouTube and Facebook are undoubtedly trying to use AI to recognise problematic language, and some of this may lead to tools we can use to highlight issues. However, as editors and proofreaders we are looking to improve poor language choices and make it more conscious. Looking at how the Editor function in MS Word and Grammarly have developed, they certainly point to a way forward. While I am not convinced a machine is going to take my job for some time, I can certainly see where it could make progress. I think the challenge of issues like conscious language is that they have too many subtleties, and the human ability to make judgements about these, and even to have a productive discussion with an author about a passage, means a human editor will continue to be able to add something a machine cannot to a piece of writing, for the foreseeable future.

About Andy Coulson

Andy Coulson is a reformed engineer and primary teacher, and a Professional Member of CIEP. He is a copyeditor and proofreader specialising In STEM subjects and odd formats like LaTeX.

About the CIEP

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Photo credits: chess by Bru-nO on Pixabay, robot by mohamed_hassan on Pixabay, Go by Elena Popova on Unsplash.

Posted by Harriet Power, CIEP information commissioning editor.

The views expressed here do not necessarily reflect those of the CIEP.