I don’t trust AI. Even its name is misleading because it implies that AI is one homogeneous thing, and that is far from the truth. The global multinational computer company IBM describes AI as a series of concepts that developed over the last 70 years. The concept of AI was initially defined in the 1950s. In the 1980s machine learning was defined, i.e. AI systems that learn from data. Then in the 2010s deep learning was developed: a form of machine learning that mimics human brain function. And in the 2020s deep learning was used to develop generative AI (aka Gen AI), i.e. large language models that can synthesise “new” content from existing data. I put “new” in inverted commas because content synthesised by a computer from existing data is not in fact new, it is simply remixed. If Gen AI output is repurposed as Gen AI input too many times, that can lead to ‘knowledge collapse’. This is a point where the diversity of available knowledge and understanding has diminished so much that outputs are no longer useful.

Gen AI also produces lies at times. These are commonly called ‘hallucinations’, probably to try to embed the concept that Gen AI is a kind of brain. This worries me because hallucinations are closely identified with some mental illnesses and the use of illegal drugs, so there is an implicit suggestion that “normal people” or “most people” would be able to recognise them. And indeed some Gen AI ‘hallucinations’ seem unmissable, like its inability to produce realistic images of hands, or suggestions that astronauts met cats when they landed on the moon. But others may seem very real, particularly as Gen AI ‘hallucinations’ will be presented to users in the same way as accurate information. This makes me wonder how many of Gen AI’s ‘hallucinations’ are going undetected. Even Professors who are experts in using Gen AI, such as Ethan Mollick, admit they can be taken in. The BBC has just published research in which Gen AI had access to the BBC website and was asked questions and given tasks about the news. Almost one in five of those answers introduced factual errors, over half involved ‘significant issues’, and over 90% contained at least some problematic content. There is a serious risk here of misinformation and we’re dealing with enough of that from human beings; we don’t need computers adding to the problem.

Gen AI also steals. Not directly, to be fair, but it is certainly in possession of stolen goods. This blog is written under a Creative Commons licence 4.0 which permits reuse, even for commercial purposes, as long as appropriate credit is given. ‘Appropriate credit’ includes my name and a link to the material. I cannot prove that my blog has been used to train Gen AI, but I bet it has, and I also bet the resulting material does not include my name or a link to my blog. Also, my conventionally published books are subject to copyright laws which prohibit their sale or use, beyond the terms of my contract with the publishers, without my permission. Yet the books I have written and commissioned for Routledge, and the articles I have written and co-written for journals published by Taylor & Francis, formed part of a deal worth millions of pounds made by their parent company Informa to sell access to their academic content for Gen AI training purposes. My contracts with Routledge do not mention AI training, I was not asked for my consent, and I have not seen a single penny of the income received or generated by this deal. Neither have any other authors I know, and some, like my co-author Janet Salmons, are very very angry.

And Gen AI both cheats and enables cheating. Gen AI has enabled students to cheat on assignments, homework, and tests. Also fraudulent AI-generated data is increasingly problematic for researchers who collect data through online surveys. There are many other examples too.

In humans, lying, stealing, and cheating are toxic behaviours. People-pleasing is another toxic human behaviour which also appears in Gen AI. Gen AI is designed to please its human operators, which can lead to ‘fake alignment’ i.e. giving different answers under different conditions rather than sticking to the truth.

Because I don’t trust Gen AI, I have never used it. I should, however, acknowledge that the opposite may be true: it may be that because I have never used Gen AI, I don’t trust it. Some people I know personally, and for whose integrity I have the utmost respect, advocate using Gen AI. Inger Mewburn, aka The Thesis Whisperer, finds it very useful. Mark Carrigan has written an excellent book, Generative AI for Academics, which recommends that readers experiment with Gen AI to assess its potential for themselves. I do not know Ethan Mollick except through his work but he too seems like a person of integrity. He has written a book, Co-Intelligence: Living and Working with AI, which recommends that readers always invite AI to the table. And he has a useful blog about AI, One Useful Thing, where he publishes his latest discoveries and thinking.

Gen AI makes computers seem like they understand us. Inger Mewburn calls Claude her work husband; Ethan Mollick recommends that we treat Gen AI like a person.  But computers are not people and I think this conflation is potentially dangerous. Computers don’t understand anything, they simply produce content in response to patterns in their training data. For me, it makes more sense to treat Gen AI as the machines they are: non-sentient, but able to mimic sentience at times.

Also, Gen AI has not been created for our benefit. Although there are undoubtedly benefits we can derive from using Gen AI, it has been created primarily to make money for big corporations and their shareholders. And it is doing that very successfully at present, not only through deals like the one Informa struck with Microsoft, but also because its primary benefits seem to be improving efficiency and productivity while reducing costs – and therefore increasing profits. And increases in the profits of global organisations benefit the few, not the many.

So for all these reasons, I have not used Gen AI, and I do not intend to any time soon. This is primarily because I think its features are more unethical than not. (And we haven’t even talked about data centres and their environmental impact.) Though I am watching and listening, and will be happy to change my mind as soon as I see some evidence that Gen AI’s features have become more ethical than unethical.

If you’ve never organised a conference before, the chances are you have no idea how much work is involved. It takes at least a year; more if it’s your first one. Good venues and good keynote speakers are usually booked up a year or more in advance, and both are crucial to the success of the event. I am already starting to think about the keynote speakers for the 2026 International Creative Research Methods Conference (ICRMC) which is still 19 months away.

Immediately after an annual conference has been held, there is a bunch of work to do: thanking keynote speakers and sponsors, preparing videos for sharing, signing a contract with the venue for the following year, and working on the call for proposals for the next conference. ICRMC is held in the second week of September and we aim to publicise the call for the following year at the start of October, with a deadline of early December. (Every year we get anguished messages, for weeks after the deadline, from people who have missed it – we had a couple just a week or so ago – so if you might be one of those people in future years, make a note now!)

While the call for proposals is out, the conference team are seeking sponsorship. Sponsorship is useful for a range of reasons: sometimes sponsors want to fund useful things like bursaries or the printing of the conference programme; institutional sponsors lend credibility; sponsors often provide merch for the goody bags. (We have had more difficulty than usual in finding sponsors this year, and so far we only have a very small amount of funding for bursaries. If you know of any individual or organisation that might be interested in sponsoring ICRMC25, please get in touch.)

In early January a small group of us meet to assess the proposals we have received. Then the conference programme needs to be put together which is a particularly complicated job for this conference. That is because (a) we let people choose how long they want, in multiples of 15 minutes, from 15-90 minutes, and (b) it is a hybrid conference so we need to create a good conference-within-a-conference for people who are attending online. So the programme can take two or three weeks to finalise.

In early March tickets go on sale, so bookings, and applications for bursaries, start to come in. Queries do too: which band do I fall into for payment, can I bring my breastfeeding baby, is there an induction loop, etc etc etc.

Over the following six months there are plenty of jobs to keep us busy. We need to order new conference bags and make sure we have enough good quality merch to go in them; prepare the virtual ‘goody bag’ with links and discounts from sponsors and presenters; make decisions about the bursary applications and communicate those decisions to the applicants; liaise with the venue about people’s dietary requirements; and so on. And the queries keep coming.

Throughout this whole time, promo is happening on social media, in newsletters, and anywhere else we can advertise the conference. Then the weekend before the conference is very busy with printing programmes and name badge inserts, making up name badges, filling goody bags, and managing the inevitable last-minute crises such as a presenter having to drop out and needing to be replaced. (Or worse, a keynote speaker, though fortunately that hasn’t happened yet and I hope it never will.)

And then we’re off to Manchester, already exhausted but also excited and with enough adrenalin to see us through. We have two wonderful days with a delightful group of like-minded people from around the world, which makes it all worthwhile. Then the whole thing starts all over again!