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The Abomination of AI – part 8 – summary and recap

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This final post recaps what we’ve learnt about the runaway nature of the AI industry, how it undermines free markets, and how we can make a difference. The core question is not what can AI do, but what should AI do?

This is the last of the series of blogs based on my keynote “The abomination of AI” at ICoSCI 2026.  Each has an accompanying segment of the video and slides from the talk as well as detailed notes and references.  Section numbers refer to the full report which will be released in the final blog.   The slide thumbnails in the text correspond to the slides in the navigation panel below.  The presentation can be played below, or opened full screen. The full length video, complete slides and further information can be found at: https://alandix.com/academic/talks/ICOSCI-2026-abomination-of-AI/

Previously …

§1.  Every industry is driven by profits and power, but there is something about the nature of AI itself, which interacts with the nature of market forces in the world that is problematic and is different from other technologies.

§2.  Can any technology be neutral?  AI can be used for good purposes, such as advances in healthcare.  It can also have bad outcomes such as bias in the criminal justice system or online exploitative pornography.  Perhaps most often it is creating the frivolous or even ugly.

§3.  The obvious impact of AI is in the things it does directly. Some technologies also change the very nature of society, affecting even those who do not use them. Cars are an obvious example.  AI is also such a technology.

§4.  Doomsayers worry about the point when AI becomes sentient, outgrowing its creators.  The real danger is more insidious: the massive financial and human impacts of AI seem almost obscene.

§5.   Network externalities, the way one person’s use of AI and digital tech changes its value for others, creates positive feedback loops, leading to runaway growth and emergent monopolies, the nemesis of free markets. This the very nature of digital technology and AI breaks free markets leading to runaway inequality, even with the best intentions of industry … but some tech companies further exploit these effects.

§6.   Runaway growth of AI is not painless – opportunity costs of investment and human costs of lost jobs.  Gains may be transitory – buy-now-pay-later tech risk tying users into spiralling costs.

§7.   It all seems too big, requiring national and international responses.  But we can make a difference using appropriately chosen small AI (including none). Plus, this good use of AI is good for business too.

8.  In summary

So, in summary,  AI can do amazing good things, but often also bad things.

More crucial is how AI is shaping society.  We have to think explicitly about this, because AI has its own dynamic.  That dynamic is not good by its nature, so we have to control it to make it serve society. Some of this needs action at a governmental and intergovernmental level, because this is so big compared to most countries.

However there are things you can do.  You can choose to sometimes not use AI or use small AI, but always to try to use AI appropriately rather than just throwing it at a problem and wiping your hands of the wider impact.

The core question is not to think solely about what can AI do?  While it still does some things frighteningly badly, AI is getting better and better at doing more and more.

But the big question, the Big, BIG question is what should AI do?

And that’s the question we need to ask ourselves continually both in our individual work and at societal level.

AI will be an abomination, but only if we let it be.

Updates

It is now four months since I gave the talk at ICoSCI 2026 on which this blog series has been based.  In that short time there have been many changes, some strengthening the arguments and some challenging them.   In addition, I’ve had helpful feedback from several people, especially extensive comments by Mark Bernstein; so many thanks to Mark and others who have engaged with this series.

I’ve written updates at the end of several blogs.  In part 3 “A different kind of apocalypse” recent reports of agentic AI ignoring guardrails make Terminator-style AI devastation, seem less distant.  Of course, since those updates the publicity around Claude Mythos’ ability to find bugs in established codebases meant that Anthropic deemed it too dangerous to release without allowing selected partners to use it first to check their own security [An26a].  While this may be in part a PR exercise, it is being taken seriously by government and pan-government organisations [AISI26,Go26].  Furthermore, as well as these external threats, Anthropic are also monitoring for the potential of AI ‘sabotage’, that is:

“when an AI model with access to powerful affordances within an organization uses its affordances to autonomously exploit, manipulate, or tamper with that organization’s systems or decision-making in a way that raises the risk of future catastrophic outcomes”  [An26b]

Updates at the end of part 6 “should we worry?“ reinforce the difficulty of switching AI models and the way OpenClaw has emphasised the under-pricing of AI use plans, and hence the way that these might adjust (upwards!) over time, just as we’ve seen with other forms of digital technology.  At the end of part 7 “what can we do?” there is a lovely example of really smart AI, combining AI and plain old computing to achieve better and cheaper outcomes.

In addition to these updates, recent developments (since the updates!) and comments have raised a couple of issues that I’d like to address.

Size matters

Since giving the talk in January, there have been further developments in reducing the costs of AI, not least DeepSeek’s V4 release, which has focused on making model training and execution more efficient [DS26].  Nvidia have released open source models designed to run on local small-scale (as in not more than a few $1000 Nvidia chips) installations [Ca26,Br26], some of these are designed for specialised applications, but some more general purpose; it could be that they are positioning themselves to spread their market beyond the small number of AI software mega-corporations, but in the process may weaken the emergent monopolies of these software players.  However Nvidia’s own near monopoly AI hardware position is being challenged by DeepSeek’s use of Huawei chips [CC26].

This seems to suggest several potential scenarios:

  1. The big players (OpenAI, Anthropic) see off the cheaper, but less powerful alternatives, maybe using market dominance to retain near monopoly positions as discussed in section 5 (blog part 4 and part 5)
  2. The lean, mean models become powerful enough to open up the market fully, so that the current mega companies and their investors lose their ‘bet’ on market dominance, leading to massive drops in their market values, and potentially a major stock exchange crash.
  3. The cheaper models become viable alternatives, but do not immediately compete on sheer power and corporate commitment leaving the mega AI corporations strong, but less all encompassing, and making the individual solution strategies in part 7 easier.

The impact of (2) on the global economy would be pretty disastrous, especially following the massive hits of US-Israel/Iran and Ukraine/Russia wars, so, on balance, the softer movement of scenario (3) feels like the best outcome.

Perversely, the big AI companies would be likely to weather the storm of (2) as the investment already committed provides a cash buffer. This was certainly the case during the dot-com period in which tech companies with second/third round investment before the crash often survived, including Lastminute.com, the IPO of which triggered the market re-evaluation of tech in 2000.  The founders and early funders will see paper devaluations, but otherwise still be in control of huge businesses; the smaller, more recent investors will lose however, including many global pension funds.

Cats and Consummate Consumerism

Section 3 (blog part 2) is, in part, quite dismissive of the vast volume of ‘frivolous’ use of generative AI.  Later, I hope that is clarified (e.g. the example of the Doctor’s Kitchen app) that this does not mean criticising all personal use of AI — appropriate use of AI in can be very beneficial, in particular allowing far more individualised access to digital technology.  Indeed, LLMs are already democratising access to many forms of professional advice that are beyond the reach of individuals and small businesses [Fu26].

However, that does leave the cats.   If that is what people want to create and view, surely that is their business?

In some ways these uses of AI are the ultimate form of consumerism — like the boxfuls of unused plastic toys, kitchen appliances that lie in the dark recess of cupboards, the 1.6 billion items of clothing in UK wardrobes that have never been worn [BBC22] — but now all digital, thrust before us by the relentless algorithms of social media.  Items we never knew we wanted instantly become essential, produced apparently for free and provided in precisely the quantity and kind that makes us want more.

Is this a choice, when the algorithms know how to nudge and channel us [HS26], when LLMs have learnt the lessons of the confidence trickster, and when the content itself is addictive [KK25]?  Is this a free market equivalent of Opium Wars?

For individuals many of the costs are effectively hidden, especially at the point of use.  Just as no fleece wearer or takeaway coffee drinker deliberately chooses to put microplastics in breast milk, the environmental and social impacts of digital and AI products are often physically and temporally distant and in many cases suffered by others [Ma24].

This distancing is in part due to digital communication and in part the diffuse relationship between the loci of production and use, especially when a large proportion of cost is in training.  However, the distancing is in part deliberate, not least the under-pricing of services to build reliance, a trick that has been part of digital products almost since their onset and very much in the playbook of the neighbourhood drug dealer.

One reason for listing the almost unbelievable facts and figures of AI growth in part 3 (§4,2) is to force us to face these choices explicitly.

The speed of change …

As is evident things are moving rapidly. This said although the details are changing, many of the large-scale impacts of AI on society and economics outlined in this series build on longer term trends in digital technology that have been evident at least since the turn of the Millennium.

With so many technologies in the past the societal impacts have only become apparent in hindsight.  With AI there are surprises, especially in terms of its spurts of almost unimaginably rapid progress, but also we are increasingly aware of the dangers and pitfalls.  The issues describe here are part of this conversation, aiming to ensure that we enter this exciting and dangerous time with eyes wide open.

Coming soon …

If you are interested in these issues, look out for the book AI or Social Justice, which Clara Crivellaro and I are currently working on.  The book website already includes a growing collection of resources including case studies and videos. .

References

[AISI26] AI Security Institute (2026) Our evaluation of Claude Mythos Preview’s cyber capabilities. AI Security Institute, Department of Science, Innovation and Technology. Apr 13, 2026. https://www.aisi.gov.uk/blog/our-evaluation-of-claude-mythos-previews-cyber-capabilities

[An26a]  Anthropic (2026).  Project Glasswing: Securing critical software for the AI era. Accessed 4th May 2026. https://www.anthropic.com/glasswing

[An26b]  Anthropic (2026).  Sabotage Risk Report: Claude Opus 4.6. Accessed 4th May 2026.   https://anthropic.com/claude-opus-4-6-risk-report

[BBC22]  BBC News (2022).  UK wardrobes stuffed with unworn clothes, study shows.  BBC News, 7 October 2022. https://www.bbc.co.uk/news/science-environment-63170952

[Br26]  Kari Briski (2026).  NVIDIA Launches Nemotron 3 Nano Omni Model, Unifying Vision, Audio and Language for up to 9x More Efficient AI Agents.  Nvidia blog.  April 28, 2026.  https://blogs.nvidia.com/blog/nemotron-3-nano-omni-multimodal-ai-agents/

[CC26]  Caiwei Chen (2026).  Three reasons why DeepSeek’s new model matters: The long-awaited V4 is more efficient and a win for Chinese chipmakers.  MIT Technology Review, April 24, 2026  https://www.technologyreview.com/2026/04/24/1136422/why-deepseeks-v4-matters/

[Ca26] Bryan Catanzaro (2026). NVIDIA Launches Open Models and Data to Accelerate AI Innovation Across Language, Biology and Robotics.  NVIDAI Blog, October 28, 2025.  https://blogs.nvidia.com/blog/open-models-data-ai/

[DS26]  DeepSeek-AI (2026).  DeepSeek-V4:Towards Highly Efficient Million-Token Context Intelligence.  Accessed 29th April 2026.  https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf

[Fu26]  Maria Ines Fuenmayor (2026). The Privilege of Refusing AI. Codified, Medum,  Apr 22, 2026.  https://codifiedai.substack.com/p/the-privilege-of-refusing-ai

[Go26]  Gordon M. Goldstein (2026). Six Reasons Claude Mythos Is an Inflection Point for AI—and Global Security.  Council on Foreign Relations, April 15, 2026. https://www.cfr.org/articles/six-reasons-claude-mythos-is-an-inflection-point-for-ai-and-global-security

[HS26]  Kali Hays, Nardine Saad and Regan Morris, (2026).  Campaigners welcome Meta and YouTube’s defeat in landmark social media addiction trial.  BBC News, 25 March 2026.  https://www.bbc.co.uk/news/articles/c747x7gz249o

[KK25]  Kooli, Chokri, Youssef Kooli, and Eya Kooli (2025). Generative artificial intelligence addiction syndrome: A new behavioral disorder?.  Asian Journal of Psychiatry 107:104476.  https://doi.org/10.1016/j.ajp.2025.104476

[Ma24]  Murgia, Madhumita (2024). Code Dependent: How AI Is Changing Our Lives. Picador.

 

The Abomination of AI – part 7 – what can we do?

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It all seems too big, requiring national and international responses.  But we can make a difference using appropriately chosen small AI (including none). Plus, this good use of AI is good for business too.

This is the seventh of a series of blogs based on my keynote “The abomination of AI” at ICoSCI 2026.  Each has an accompanying segment of the video and slides from the talk as well as detailed notes and references.  Section numbers refer to the full report which will be released in the final blog.   The slide thumbnails in the text correspond to the slides in the navigation panel below.  The presentation can be played below, or opened full screen. The full length video, complete slides and further information can be found at: https://alandix.com/academic/talks/ICOSCI-2026-abomination-of-AI/

Previously …

§1.  Every industry is driven by profits and power, but there is something about the nature of AI itself, which interacts with the nature of market forces in the world that is problematic and is different from other technologies.

§2.  Can any technology be neutral?  AI can be used for good purposes, such as advances in healthcare.  It can also have bad outcomes such as bias in the criminal justice system or online exploitative pornography.  Perhaps most often it is creating the frivolous or even ugly.

§3.  The obvious impact of AI is in the things it does directly. Some technologies also change the very nature of society, affecting even those who do not use them. Cars are an obvious example.  AI is also such a technology.

§4.  Doomsayers worry about the point when AI becomes sentient, outgrowing its creators.  The real danger is more insidious: the massive financial and human impacts of AI seem almost obscene.

§5.   Network externalities, the way one person’s use of AI and digital tech changes its value for others, creates positive feedback loops, leading to runaway growth and emergent monopolies, the nemesis of free markets. This the very nature of digital technology and AI breaks free markets leading to runaway inequality, even with the best intentions of industry … but some tech companies further exploit these effects.

§6.   Runaway growth of AI is not painless – opportunity costs of investment and human costs of lost jobs.  Gains may be transitory – buy-now-pay-later tech risk tying users into spiralling costs.

 

7.  What can we do?

These issues all seem too big, frighteningly so.   So what can you do?

You might be a policy maker, or on a government committee that’s advising governments.  If so, you might be in position to make changes at that scale.  Most do not have such high-level influence, but there are changes you can make within your own spheres to help ameliorate some of these potential dangers.  I’ll focus on the UX designer or AI developer, but some of the ideas are ones you might be able to adopt in your own personal use or within an organisation.

 

7.1  No AI

One option is to simply, say “no” to AI.

If you are a designer, ask, “do I need AI at all in my project?”  Of course, everybody now expects every product to say ‘AI powered’, so you may not be able to avoid AI altogether, but it could be very simple AI.  But do ask whether you need it at all, if you don’t, why are you feeling you need to use it?

 

7.2  Small AI

If you do decide to use AI, you can opt for small AI.

If you are using language models or other generative AI, you might use smaller models, the kind that have been deliberately designed to be able to run on less powerful hardware. There are many good reasons to do this.  Indeed, Apple have been encouraging smaller AI because they want the AI to run on people’s personal devices, not just in the cloud.  This is because privacy is a strong part of the company brand.

Where it is appropriate you could use traditional AI, which is usually much smaller in terms of memory and computation.

Purely from a technical perspective, there are some really interesting research challenges in this area, both in terms of human computer interaction (see my 2024 talk on ‘Patient Interaction’ [Dx24]) and also pure technical AI.

Images: [Di22,Sa23,Dx25,DS25]

You’ll have seen some of the modifications of algorithms that are transforming this landscape including open LLaMa [ZD22,TH23], LORA [HS22] and LiGO [WP23].  DeepSeek [DS24,DS25] made waves when US export restrictions on NVIDIA chips forced Chinese innovators to adopt a far leaner and smarter approach to LLM development [LF24].  Debatably DeepSeek’s learning might have piggybacked off some of the other LLMs [We25b], but certainly at execution time it used far less resources than other LLMs at time. Now other LLMs have adopted lessons from DeepSeek, and all are looking to perform more efficiently, so there is small shift in thinking away from a simplistic ‘bigger is better’ approach [Hi20].

 

7.3  When to use AI

There is also a choice about when to use AI.

The most obvious use of AI is at execution time in a user interface or delivered application as part of the service provided.  This can of course be small AI or no AI at all even.

But you can also use AI at design time.  You might use big AI to create small AI for the delivered system, for example using techniques to compress the model.  You can also use AI as part of the UX process to critique a user interface, create rapid prototypes, or propose design ideas [Dx26b].  In addition, AI-based coding tools can create AI-free (or low-AI) systems.

Crucially, if you use big AI to help create a (smaller) product, it effectively gets reused again and again and again and again.   So it’s less expensive – both moneywise expensive to a company, but also less expensive in terms of its impact on the environment and society.

In fact, this is really powerful use of AI. For instance, one of the things I argue elsewhere is that AI critiques of UIs will be far better for accessibility than even the best designers.  This is in part because it is really hard for us to think about even obvious diversity such as what’s it like to be blind or deaf, or have a physical disability, an automated design tool can check a concept or prototype against vast numbers of different types of perceptual and physical abilities as well as combinations.  Even more important, it is almost impossible for us to imagine what it’s like to be somebody who thinks differently, for example somewhere distant from ourselves in a neurodivergent space .  I don’t think AI will be good at this, but I think it will be better than we are.

 

7.4  How to use AI

Finally, if you are using AI think carefully about the kind of AI you are going to use, and how to incorporate it into a system.  For many years I’ve talked about appropriate intelligence, most often in relation to AI error and the need to design human–AI systems that together are robust and effective, not focusing in the AI accuracy alone [DB00,BD23].  However, the same lesson can be applied more broadly.

Often, we think about human interaction with AI, but it can be useful to think of a three-way interaction with human(s), AI and plain-old computing – that is hand coded algorithms or classic AI. Now look at each kind of AI that you are thinking of using and ask what is it good for?

What kind of things do I mean? One of the problems with traditional AI is that it was good with hard-nosed rules, but much more problematic with fuzzy things.  There are various techniques such as Bayesian methods and fuzzy logic, but they require you to formalize the fuzziness into probabilities or similar functions.  Amongst other things this limited various forms of natural language understanding and common-sense reasoning

Of course, large language models are really good at dealing with the nuances of language, but LLMs are less good when they try to be very precise, not least because they keep hallucinating!

So as you design for AI, ask what is it good for, how can I use it most appropriately?

As an example of the appropriate use of AI,  my wife uses an app from “The Doctor’s Kitchen” (https://www.thedoctorskitchen.com) to help keep track of the health value of food.

You take a photo of a plate of food before you eat it and the app creates a report on its nutritional value: how much fibre and protein it contains and its inflammation index.  Is it likely to be good for you or bad?

You could imagine doing this by writing a complicated prompt to an LLM or train a deep learning algorithm with lots of plates of food and hand-curated reports.  The app does not work like that.

What it does is to use image processing AI to analyse the plate and work out what food is on it.  Indeed, you can press an edit button to see what it thinks you’ve got on your plate, and, if it’s got it wrong, edit it.  One assumes that a log of these edits helps to further train the image processing AI.

So the AI has been used for the fuzzy part of the task, working out that there are crisps on the plate but a no cake. It even manages to recognise hummus and estimate how much.  It is amazingly good, but does sometimes get things wrong in terms of the volume or even what is there; however, when that happens you can easily see and correct it.

So this is using AI for the fuzzy bit.

This table of contents will then go into a standard algorithm that uses tables of nutritional values to look up how much protein is in, say, 10 grams of almonds, add this up for the plate and hence generate the final nutritional report.

AI and traditional computing together — combining the two using the best aspects of each.

Note that this is more explainable, you know, what’s going on.

It is also more flexible in terms of you can choose to enhance different components and change others.

There is also less vendor tie in.  This is not removed entirely as you need a new AI to be retrained.  However, it is easier to swap just the food recognition part than if the whole system were in a single AI.

This is good from a business point of view, but it also means you are using less large-scale AI with its environmental, financial and democratically damaging effects, when you could be using simpler computation.

Coming next …

Part 8 – summary and recap

This final post will recap what we’ve learnt about the runaway nature of the AI industry, how it undermines free markets, and how we can make a difference. The core question is not what can AI do, but what should AI do?

 

Update

Since the talk in January I read about A.T.L.A.S. (Adaptive Test-time Learning and Autonomous Specialization) an AI coding system built by a business student Johnathon Tigges wanting to challenge the assumption that “only the biggest players can build meaningful things” [Ti26] .  It is able to outcompete the big coding agents by being clever – rather than just throwing a problem into a big code-optimised LLMs and asking for a solution, it uses AI to generate lots of potential code fragments and tests them, using the best to further refine the AI model … all on a consume GPU.  A lovely example of smart use of AI!  For a more detailed description see Sebastian Buzdugan’s Medium story about it [Bu26].

References

[BD23] Alba Bisante, Alan Dix, Emanuele Panizzi, and Stefano Zeppieri (2023). To err is AI.In Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter, pp.1–11. https://dsoi.org/10.1145/3605390.3605414

[Bu26] Sebastian Buzdugan (2026). Why a $500 GPU Can Beat Claude Sonnet on Coding Benchmarks. Medium. Mar 28, 2026. https://medium.com/@sebuzdugan/why-a-500-gpu-can-beat-claude-sonnet-on-coding-benchmarks-6c8169ffe4fe

[DS24]  DeepSeek-AI (2024).  DeepSeek-V3 Technical Report. arXiv preprint. 27 Dec 2024. https://arxiv.org/abs/2412.19437

[DS25]  DeepSeek-AI (2025).  DeepSeek-V3. GitHub Repository. Release v1.0.0. 27 Jun 2025. https://github.com/deepseek-ai/DeepSeek-V3

[Di22] Dickson, B. (2022). Can large language models be democratized? TechTalk,-May 16, 2022. https://bdtechtalks.com/2022/05/16/opt-175b-large-language-models/

[DB00] A. Dix, R. Beale and A. Wood (2000).  Architectures to make Simple Visualisations using Simple Systems.  Proceedings of Advanced Visual Interfaces – AVI2000, ACM Press, pp. 51-60.  https://www.alandix.com/academic/papers/avi2000/

[Dx24] Alan Dix (2024). Patient Interaction – for well-being, productivity and sustainability. FUSION 2024, Kuala Lumpur, Malaysia, 28 Sept. 2024. https://www.alandix.com/academic/talks/FUSION2024/

[Dx25]  Dix, A. (2025). Artificial Intelligence – Humans at the Heart of Algorithms, 2nd Edition, Chapman and Hall.  https://alandix.com/aibook/

[bibitelm name=Dx26b] A. Dix. (2026). AI for Human–Computer Interaction. CRC Press, in press. https://alandix.com/ai4hci/

[Hi20] Hinton, G. (2020). Extrapolating the spectacular performance of GPT3 into the future suggests that the answer to life, the universe and everything is just 4.398 trillion parameters. Twitter (now X), Jun 10, 2020. https://x.com/geoffreyhinton/status/1270814602931187715

[HS22]  Hu, E. J., Shen, Y., et al. (2022). LoRa: Low-rank adaptation of large language models. ICLR, 1(2), 3. https://arxiv.org/abs/2106.09685

[LF24] Liu, A., Feng, B., et al. (2024). Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. https://arxiv.org/abs/2412.19437

[Sa23] Sajid, H. (2023).  Artificial Intelligence: Can You Build Large Language Models Like ChatGPT At Half Cost? Unite.ai, May 11, 2023.  https://www.unite.ai/can-you-build-large-language-models-like-chatgpt-at-half-cost/

[Ti26]  Johnathon Tigges (2026).  A.T.L.A.S. – Adaptive Test-time Learning and Autonomous Specialization. GitHub. https://github.com/itigges22/ATLAS

[TH23] Touvron, H., Martin, L., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288. https://arxiv.org/abs/2307.09288

[WP23] Wang, P., Panda, R., et al. (2023). Learning to grow pretrained models for efficient transformer training. arXiv preprint.  https://arxiv.org/abs/2303.00980

[We25b] Werner, J. (2025). Did DeepSeek Copy Off Of OpenAI? And What Is Distillation? Forbes, Jan 30, 2025. https://www.forbes.com/sites/johnwerner/2025/01/30/did-deepseek-copy-off-of-openai-and-what-is-distillation/

[ZD22]  Zhang, S., Diab, M. and Zettlemoyer, L. (2022). Democratizing access to large-scale language models with OPT-175B. Meta Research Blog, May 3, 2022. https://ai.meta.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/

 

 

 

The Abomination of AI – part 6 – should we worry?

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Runaway growth of AI is not painless – opportunity costs of investment and human costs of lost jobs.  Gains may be transitory – buy-now-pay-later tech risk tying users into spiralling costs.

This is the sixth of a series of blogs based on my keynote “The abomination of AI” at ICoSCI 2026.  Each has an accompanying segment of the video and slides from the talk as well as detailed notes and references.  Section numbers refer to the full report which will be released in the final blog.   The slide thumbnails in the text correspond to the slides in the navigation panel below.  The presentation can be played below, or opened full screen. The full length video, complete slides and further information can be found at: https://alandix.com/academic/talks/ICOSCI-2026-abomination-of-AI/

Previously …

§1.  Every industry is driven by profits and power, but there is something about the nature of AI itself, which interacts with the nature of market forces in the world that is problematic and is different from other technologies.

§2.  Can any technology be neutral?  AI can be used for good purposes, such as advances in healthcare.  It can also have bad outcomes such as bias in the criminal justice system or online exploitative pornography.  Perhaps most often it is creating the frivolous or even ugly.

§3.  The obvious impact of AI is in the things it does directly. Some technologies also change the very nature of society, affecting even those who do not use them. Cars are an obvious example.  AI is also such a technology.

§4.  Doomsayers worry about the point when AI becomes sentient, outgrowing its creators.  The real danger is more insidious: the massive financial and human impacts of AI seem almost obscene.

§5 .   Network externalities, the way one person’s use of AI and digital tech changes its value for others, creates positive feedback loops, leading to runaway growth and emergent monopolies, the nemesis of free markets. This the very nature of digital technology and AI breaks free markets leading to runaway inequality, even with the best intentions of industry … but some tech companies further exploit these effects.

6.  Should we worry?

6.1  Jobs and power

Image: Scottish Government, CC BY 2.0. https://commons.wikimedia.org/wiki/File:One_of_the_typing_pools_%283829002585%29.jpg

Does this matter?  So what if a small number of companies have notional multi-trillion balance sheets and are engaged in runaway development in the digital realm, so long as it doesn’t affect the real world.  But, of course, it does; the digital domain is leaking into the physical domain.

Of course, technology and automation have long had massive impact on society, with a gradual shift from human expertise to financial capital.  This certainly dates back to the 19th century or late 18th century with the rise of the industrial revolution.  Of course, at that time, humans were still needed, but they went from being the expert weavers and spinners, to the those (including young children) who merely tended the machines, monitoring and knotting broken threads, and occasionally losing arms in the moving parts.  So it wasn’t that the humans were unnecessary, but they merely fed the machines.

Moving into the 20th century, machines replaced humans more completely with fully automated production lines and industrial robots, although of course still with humans cleaning up between them.  In many parts of the global north skilled manual work has all but disappeared, with a combination of automation and out-sourcing.

To some extent the impact of automation initially hit traditional male jobs, but in the latter half of 20th century, from about the early seventies on this also hit clerical roles.  Until then every big organisation would have had a typing pool.  My own mother was for many years a typist at first in the War Department throughout the Second World War, and then the Inland Revenue. These typing pools, consisted of ranks of people, usually women, typing sometimes from dictation and shorthand, and sometimes other forms of handwriting.  Word processors basically destroyed the typing pool. Whereas previously, managers would dictate letters and reports to a secretary who would then type it up,  with the word processor, despite initial resistance, they would type directly themselves.  Of course, after initial diversity, Microsoft Word soon became dominant – another emergent monopoly, although now matched by Google Workspace, with about 90% world share between them.

So, in general, skilled working class jobs have been destroyed by automation leaving a growing underclass with minimum wage jobs and gig work.

What we’re seeing now is that the mid-range intellectual work is starting to be eaten by AI [BSI25,].

You may have seen the MIT report that found that while many companies were investing heavily in AI, around 95% of the projects were considered to be failing or underperforming [CP25].  So, effective job substitution is not yet universal, but in some areas such as computing many of the lower range of the roles, typical graduate first jobs, are being replaced by AI.  Until recently the expert developer would have several junior developers who do the grunt work; now this is done by AI.  Similar pictures are emerging in advertising, aspects of finance, and some of the large management consultancies [Ko25,Sw26,IPA26,KM26,Pa26].  In the UK, and even more so in other parts of the world, there are strong pushes to use AI more extensively within government, not least on the assumption that it will improve efficiency [GUK25,Dx26].

There’s a critical issue about who’s in control.  Think about the road network.  In the UK there are some private roads and also some toll roads, but the majority of roads, including almost all in urban areas, are owned by the local authority or central government.  That is, the vast majority of the road network is local in terms of its maintenance and control.  Imagine if the road network was instead owned by two or three major companies based in the west coast of America.  Imagine if every road in Malaysia, every road in Indonesia, as well as every road in UK was owned by those two or three companies  half the world away.  If there’s a pothole in the road, it is those companies to whom you have to complain.  Perhaps they decide to charge you to use the road outside your house or decide to remove the roads entirely if they’re in dispute with you or your government.

That’s exactly the direction we are moving with AI and public services.  Even assuming the best intention of the big AI players, this does feel worrying.  And, of course, this isn’t a choice you can make or not.  Just like cars and roads, once AI is embedded into public service everything orients around it.

Returning to the changes in employment, once we lose the entry stage jobs, there’s a clear problem for the people who would have had them.  All the graduates from our universities, who would have been going into those jobs, are being hit, and, in many countries, on top of large student loans [DoE25,Pa25,Pa26].  This is creating a class of people who are underemployed, inexperienced, and quite likely disaffected with society.  Think of this in the light of the rise of extremism across the world.  Often this is dismissed as a problem of the uneducated, but here we are adding a vast number of highly educated people, who are disaffected in society, further spreading those extreme messages.

 

6.2  Locked into AI

This is also a problem within an organization.  If you are not employing those early career people, what happens in five or ten years’ time as your more experienced employees want to move up the organization?  How do you fill in those gaps if you haven’t been training people?

This might be something we need to address as universities, training people effectively to higher and higher levels so that they can jump in at that point.

Or the organisation can simply find they need more AI – what they certainly can’t do is just turn off the AI because they haven’t got the people with the experience in order to do the jobs anymore.  They have become locked in as a company to the use of AI.

This is also true of data.  Microsoft have a guide entitled, “Prepare your data for AI” [Ms26].  The use of AI is not coming for free, but needs a rearrangement of data for it.  One does wonder if the same effort making data ready for AI could be better spent making it ready for simpler statistical algorithms.

However, let’s assume you have put effort into reorienting your whole data around AI. Your systems rapidly become AI dependent – your recent information and new data has become deeply embedded into the AI itself in ways that are often opaque.

Once you have bought into an AI system, you can’t just say, “well, let’s just swap to something else”.  It’s difficult even to swap vendors once it is that embedded.

 

6.3  Buy now … pay later

If you have a loan with interest, you know you have to pay for it eventually, but things can be less obvious.  When I was little, my mum had a Kays catalogue, a sort of the 1960s  equivalent of internet selling [WA17].  Its pages were full of big colour pictures of clothes, white goods, toys, etc. …it was usually the toys I was looking at.  You could buy things from the catalogue and could pay over 20 weeks with no interest, but of course the things cost more than if you had the ready cash buy them at a shop.  So effectively you were paying extra.

AI currently is in that ‘buy now pay later’ mode, both globally and locally for individuals.  AI growth is funded by massive investment (as we discussed absolutely huge) possibly more than ever before except perhaps the South Sea Bubble.  However, the income doesn’t in any way cover the costs, and the ratio between expected income and investment is way out of kilter for what you’d expect even for a digital company, let alone for a physical one.

So how do the books add up?

If you’re an accountant in the company or if you’re an investment manager, what are you thinking about as, as you see these figures?  Why don’t you sound the alarm?  The reason is you are thinking that in the future you will have more money from that stream.  In early digital companies, like Amazon, you did that because you assumed you were going have a bigger market, the number of people who would use it would grow.

But AI already has lots of users, so instead you have two options.  The first is to find ways to make what you produce more cheaply, which is happening to some extent already. However, you don’t want it to get too cheap otherwise competitors can enter the market.  The alternative, and your only real option, to recoup your investment by charging more or getting the same customers to use more. Either way, it is the customer who pays in the end!

This is no secret.  Fortune magazine said that OpenAI’s business plan relies on “what amounts to a bet on dominance” [Sm25].  That is, in putting in all that investment, what investors are hoping is that the company will become the AI company in an area that everybody is tied into.  And then of course they can charge pretty much what they like: a buy now – pay later world. We’re using AI now, but the cost is going to come later on.

 

Coming next …

Part 7 – what can we do?

It all seems too big, requiring national and international responses.  But we can make a difference using appropriately chosen small AI (including none). Plus, this good use of AI is good for business too.

 

Update.

Since the talk, I read about a woman who had developed a close relationship with a chatbot hosted on a version of ChatGPT that is due to be retired [He26]. While she could probably export her chat history and use that to reinitialise the new version of the software, it would not be the same.  We will soon start to hear similar stories for business and public systems as tech companies have not had a good record of backward compatibility, and this is all but impossible with current LLMs.

Also, in late January, OpenClaw was released [OC26].  This highlighted the way current payment models do not reflect the actual cost of use. OpenClaw (originally called Clawdbot) is an open-source GitHub project that used the Claude API to create an automated assistant coordinating web and desktop resources.  Within days of the launch Anthropic enforced a long-standing, but unenforced, restriction on third-party use of its API and blocked OpenClaw for most user accounts including its $200 Max account.  This was because these accounts come with monthly usage limits, and OpenClawd encouraged full use of those limits.  However, the business model of even premium accounts depends on users NOT using their monthly allowances. OpenClawd encouraged full use of those limits, thus exposing.the true cost of the full use vastly exceeded the subscriptions [Ba26] .

 

 

References

[Ba26] Novy Baf (2026).  Anthropic Pushed Its Most Loyal Developers Straight Into OpenAI’s arms. OpenAI Didn’t Even Have to Ask.  The Nov TEch, 2nd Mar 2026.  https://www.thenovtech.com/p/anthropic-pushed-its-most-loyal-developers

[BSI25] British Standards Institution (2025). Evolving Together: AI, automation and  building the skilled  workforce of the future.  https://www.bsigroup.com/en-GB/insights-and-media/insights/whitepapers/evolving-together-flourishing-in-the-ai-workforce/

[Dx26] A. Dix. (2026). Beyond the Algorithm: Designing Human-Centric Public Service with AI. Talk at Service Design for Public Sector Spotlight Seminar series of challenges and opportunities between Design Cultures and Public Sector, Sapienza, University of Rome + Online, 4th February 2026. https://alandix.com/academic/talks/Rome-Seminar-Feb-2026/

[DoE25] Department of Education (2025). The impact of AI on UK jobs and training. November 2023.  https://www.gov.uk/government/publications/the-impact-of-ai-on-uk-jobs-and-training

[CP25]  Aditya Challapally, Chris Pease, Ramesh Raskar, Pradyumna Chari (2025). The GenAI Divide: State of AI in Business 2025. MIT NANDA, July 2025. https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

[GUK25] Gov.UK (2025). AI to power national renewal as government announces billions of additional investment and new plans to boost UK businesses, jobs and innovation. Press release from Department for Science, Innovation and Technology, HM Treasury, Wales Office, The Rt Hon Liz Kendall MP, The Rt Hon Rachel Reeves MP and The Rt Hon Jo Stevens MP.  20 November 2025. https://www.gov.uk/government/news/ai-to-power-national-renewal-as-government-announces-billions-of-additional-investment-and-new-plans-to-boost-uk-businesses-jobs-and-innovation

[He26]  Stephanie Hegarty (2026). Rae fell for a chatbot called Barry, but their love might die when ChatGPT-4o is switched off. BBC News, 14 February 2026. https://www.bbc.co.uk/news/articles/crl43dxwwy9o

[IPA26] IPA (2026). IPA Agency Census 2025 shows workforce declines while diversity improves.  Institute of Practitioners in Advertising. 11 February 2026. https://ipa.co.uk/news/agency-census-2025/

[KM26] Lucy Knight and Sumaiya Motara (2026). The big AI job swap: why white-collar workers are ditching their careers. The Guardian,  11 Feb 2026. https://www.theguardian.com/technology/2026/feb/11/big-ai-job-swap-white-collar-workers-ditching-their-careers

[Ko25] Saskia Koopman (2025).  Big Four slash graduate jobs as AI takes on entry level work. City AM, 23 June 2025. https://www.cityam.com/big-four-slash-graduate-jobs-as-ai-takes-on-entry-level-work/

[Ms26] Microsoft (2026). Prepare your data for AI. Dated 20/1/2026.  https://learn.microsoft.com/en-gb/power-bi/create-reports/copilot-prepare-data-ai

[OC26] OpenClaw (2026).  OpenClaw — Personal AI Assistant. https://github.com/openclaw/openclaw

[Pa25] Joanna Partridge (2025). Gen Z faces ‘job-pocalypse’ as global firms prioritise AI over new hires, report says. The Guardian,  9 Oct 2025. https://www.theguardian.com/money/2025/oct/09/gen-z-face-job-pocalypse-as-global-firms-prioritise-ai-over-new-hires-report-says

[Pa26] Joanna Partridge (2026). More than a quarter of Britons say they fear losing jobs to AI in next five years. The Guardian,  25 Jan 2026. https://www.theguardian.com/business/2026/jan/25/more-than-quarter-britons-fear-losing-jobs-ai-next-five-years

[Sm25]  Dave Smith (2025). OpenAI says it plans to report stunning annual losses through 2028—and then turn wildly profitable just two years later . Fortune, November 12, 2025. https://fortune.com/2025/11/12/openai-cash-burn-rate-annual-losses-2028-profitable-2030-financial-documents/

[Sw26] Mark Sweney (2026). UK ad agencies undergo their biggest exodus of staff as AI threatens industry. The Guardian,  13 Feb 2026. https://www.theguardian.com/media/2026/feb/13/uk-ad-agencies-biggest-annual-exodus-of-staff-ai-threatens-industry

[WA17]  Worcestershire Archive and Archaeology Service (2017).  Christmas and Kays.  Explore the Past. 19th December 2017. https://www.explorethepast.co.uk/2017/12/christmas-and-kays/

 

 

 

 

Query-by-Browsing gets local explanations

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Query-by-Browsing (QbB) now includes local explanations so that you can explore in detail how the AI generated query relates to dataset items.

Query-by-Browsing is the system envisaged in my 1992 paper that first explored the dangers of social, ethnic and gender bias in machine-learning algorithms.  QbB generates queries, in SQL or decision tree form based on examples of records that the user does or does not want. A core feature has always been the dual intensional (query) and extensional (selected data) to aid transparency.

QbB has gone through various iterations and a simple web version has been available for twenty years, and was updated last year to allow you to use your own data (uploaded as CSV files) as well as the demo datasets.

The latest iteration also includes a form of local explanation.  If you hover over a row in the data table it shows which branch of the query meant that the row was either selected or not.

Similarly hovering over the query shows you which data rows were selected by the query branch.

However, this is not the end of the story!

In about two weeks Tommaso will be presenting our paper “Talking Back: human input and explanations to interactive AI systems” at the Workshop on Adaptive eXplainable AI (AXAI) at IUI 2025 in Cagliari, Italy,  A new version of QbB will be released to coincide with this.  This will include ‘user explanations’, allowing the user to tell the system why certain records are important to help the machine learning make better decisions.

Watch this space …

 

 

 

Cheat Mastermind and Explainable AI

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How a child’s puzzle game gives insight into more human-like explanations of AI decisions

Many of you will have played Mastermind, the simple board game with coloured pegs where you have to guess a hidden pattern.  At each turn the person with the hidden pattern scores the challenge until the challenger finds the exact colours and arrangement.

As a child I imagined a variant, “Cheat Mastermind” where the hider was allowed to change the hidden pegs mid-game so long as the new arrangement is consistent with all the scores given so far.

This variant gives the hider a more strategic role, but also changes the mathematical nature of the game.  In particular, if the hider is good at their job, it makes it a worst case for the challenger if they adopt a minimax strategy.

More recently, as part of the TANGO project on hybrid human-AI decision making, we realised that the game can be used to illustrate a key requirement for explainable AI (XAI).  Nick Chater and Simon Myers at Warwick have been looking at theories of human-to-human explanations and highlighted the importance of coherence, the need for consistency between explanations we give for a decision now and future decisions.  If I explain a food choice by saying “I prefer sausages to poultry“, you would expect me to subsequently choose sausages if given a choice.

Cheat Mastermind captures this need to make our present decisions consistent with those in the past.  Of course in the simplified world of puzzles this is a perfect match, but in real world decisions things are more complex.  Our explanations are often ‘local’ in the sense they are about a decision in a particular context, but still, if future decisions disagree wit earlier explanations, we need to be able to give a reason for the exception: “turkey dinners at Christmas are traditional“.

Machine learning systems and AI offer various forms of explanation for their decisions or classifications.  In some cases it may be a nearby example from training data, in some cases a heat map of areas of an image that were most important in making a classification, or in others an explicit rule that applies locally (in the sense of ‘nearly the same data).  The way these are framed initially is very formal, although they may be expressed in more humanly understandable visualisations.

Crucially, because these start in the computer, most can be checked or even executed (in the case of rules) by the computer.  This offers several possible strategies for ensuring future consistency or at least dealing with inconsistency … all very like human ones.

  1. highlight inconsistency with previous explanations: “I know I said X before, but this is a different kind of situation”
  2. explain inconsistency with previous explanations: “I know I said X before, but this is different because of Y”
  3. constrain consistency with previous explanations by adding the previous explanation “X” as a constraint when making future decisions. This may only be possible with some kinds of machine learning algorithms.
  4. ensure consistency by using the previous explanation “X” as the decision rule when the current situation is sufficiently close; that is completely bypass the original AI system.

The last mimics a crucial aspect of human reasoning: by being forced to reflect on our unconscious (type 1) decisions, we create explicit understanding and then may use this in more conscious rational (type 2) decision making in the future.

Of course, strategy 3 is precisely Cheat Mastermind.

 

 

 

Busy September – talks, tutorials and an ultra-marathon

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September has been a full month!

During the last two weeks things have started to kick back into action, with the normal rounds of meetings and induction week for new students.  For the latter I’d pre-recorded a video welcome, so my involvement during the week was negligible.  However, in addition I delivered a “Statistics for HCI” day course organised by the BCS Interaction Group with PhD students from across the globe and also a talk “Designing User Interactions with AI: Servant, Master or Symbiosis” at the AI Summit London.  I was also very pleased to be part of the “60 faces of IFIP” campaign by the International Federation for Information Processing.

It was the first two weeks that stood out though, as I was back on Tiree for two whole weeks.  Not 100% holiday as during the stay I gave two virtual keynotes: “Qualitative–Quantitative Reasoning: thinking informally about formal things” at the International Colloquium on Theoretical Aspects of Computing (ICTAC) in Kazakhstan and “Acting out of the Box” at the University of Wales Trinity St David (UWTSD) Postgraduate Summer School.  I also gave a couple of lectures on “Modelling interactions: digital and physical” at the ICTAC School which ran just before the conference and presented a paper on “Interface Engineering for UX Professionals” in the Workshop on HCI Engineering Education (HCI-E2) at INTERACT 2021 in Bari.  Amazing how easy it is to tour the world from a little glamping pod on a remote Scottish Island.

Of course the high point was not the talks and meetings, but the annual Tiree Ultra-marathon.  I’d missed last year, so especially wonderful to be back: thirty five miles of coastline, fourteen beaches, not to mention so many friendly faces, old friends and new.  Odd of course with Covid zero-contact and social distancing – the usual excited press of bodies at the pre-race briefing in An Talla, the Tiree community hall, replaced with a video webinar and all a little more widely spaced for the start on the beach too.

The course was slightly different too, anti-clockwise and starting half way along Gott Bay, the longest beach.  Gott Bay is usually towards the end of the race, about 28 miles in, so the long run, often into the wind is one of the challenges of the race.  I recall in 2017 running the beach with 40 mile an hour head wind and stinging rain – I knew I’d be faster walking, but was determined to run every yard of beach..  Another runner came up behind me and walked in my shelter.  However, this year had its own sting in the tail with Ben Hynish, the highest point, at 26 miles in.

The first person was across the line in about four-and-a-quarter hours, the fastest time yet.  I was about five hours later!

This was my fifth time doing the ultra, but the hardest yet, maybe in part due to lockdown couch-potato-ness!  My normal training pattern is that about a month before the ultra I think, “yikes, I’ve not run for a year” and then rapidly build up the miles – not the recommended training regime!  This year I knew I wasn’t as fit as usual, so I did start in May … but then got a knee injury, then had to self-isolate … and then it was into the second-half of July; so about a month again.

Next year it will be different, I will keep running through the winter … hmm … well, time will tell!

The different September things all sound very disparate – and they are, but there are some threads and connections.

The first thread is largely motivational.

The UWTSD keynote was about the way we are not defined by the “kind of people” we think of ourselves as being, but by the things we do.  The talk used my walk around Wales in 2013 as the central example, but the ultra would have been just as pertinent.  Someone with my waistline is not who one would naturally think as being an ultramarathon runner – not that kind of person, but I did it.

However, I was not alone.  The ‘winners’ of the ultra are typically the rangy build one would expect of a long-distance runner, but beyond the front runners, there is something about the long distance that attracts a vast range of people of all ages, and all body shapes imaginable.  For many there are physical or mental health stories: relationship breakdowns, illnesses, that led them to running and through it they have found ways to believe in themselves again.  Post Covid this was even more marked: Will, who organises the ultra, said that many people burst into tears as they crossed the finish line, something he’d never seen before.

The other thread is about the mental tools we need to be a 21st century citizen.

The ICTAC keynote was about “Qualitative–Quantitative Reasoning”, which is my term for the largely informal understanding of numbers that is so important for both day-to-day and professional life, but is not part of formal education.  The big issues of our lives from Covid to Brexit to climate change need us to make sense of large-scale numerical or data-rich phenomena.  These often seem too complex to make sense of, yet are ones where we need to make appropriate choices in both our individual lives and political voices.  It is essential that we find ways to aid understanding in the public, press and politicians – including both educational resources and support tools.

The statistics course and my “Statistics for HCI” book are about precisely this issue – offering ways to make sense of often complex results of statistical analysis and obtain some of the ‘gut’ understanding that professional statisticians develop over many years.

My 60 faces of IFIP statement also follows this broad thread:

“Digital techology is now essential to being a citizen. The future of information processing is the future of everyone; so needs to be understood and shaped by all. Often ICT simply reinforces existing patterns, but technology is only useful if we can use it to radically reimagine a better world.”

More information on different events

Tiree Ultra

Tiree Ultramarathon web page and Facebook Group

Paper: Interface Engineering for UX Professionals

HCI-E2: Workshop on HCI Engineering Education – for developers, designers and more, INTERACT 2021, Bari, Italy – August 31st, 2021. See more – paper and links

Summer School Lecturea: Modelling interactions: digital and physical

Lecture at ICTAC School 2021: 18th International Colloquium on Theoretical Aspects of Computing, Nazarbayev University, Nur-Sultan, Kazakhstan, 1st September 2021. See more – abstract and links

Talk: Designing User Interactions with AI: Servant, Master or Symbiosis

The AI Summit London, 22nd Sept. 2021. See more – abstract and links

Day Course: Statistics for HCI

BCS Interaction Group One Day Course for PhD Students, 21st Sept. 2021.
See my Statistics for HCI Micro-site.

Keynote: Acting out of the Box

Rhaglen Ysgol Haf 2021 PCYDDS / UWTSD Postgraduate Summer School 2021, 10th Sept. 2021. See more – abstract and links

Keynote: Qualitative–Quantitative Reasoning: thinking informally about formal things

18th International Colloquium on Theoretical Aspects of Computing, Nazarbayev University, Nur-Sultan, Kazakhstan, 10th Sept. 2021. See more – full paper and links

Induction week greeting

 

physigrams – modelling the device unplugged

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Physigrams get their own micro-site!

See it now at at physicality.org/physigrams

Appropriate physical design can make the difference between an intuitively obvious device and one that is inscrutable.  Physigrams are a way of modelling and analysing the interactive physical characteristics of devices from TV remotes to electric kettles, filling the gap between foam prototypes and code.

Sketches or CAD allow you to model the static physical form of the device, and this can be realised in moulded blue foam, 3D printing or cardboard mock-ups.  Prototypes of the internal digital behaviour can be produced using tools such as Adobe Animate, proto.io or atomic or as hand-coded using standard web-design tools.  The digital behaviour can also be modelled using industry standard techniques such as UML.

  

Physigrams allow you to model the ‘device unplugged’ – the pure physical interaction potential of the device: the ways you can interact with buttons, dials and knobs, how you can open, slide or twist movable elements.  These physigrams can be attached to models of the digital behaviour to understand how well the physical and digital design compliment one another.

Physigrams were developed some years ago as part of the DEPtH project., a collaboration between product designers at Cardiff School of Art and Design and  computer scientists at Lancaster University. Physigrams have been described in various papers over the years.  However, with TouchIT ,our book on physicality and design (eventually!) reaching completion and due out next year, it felt that physigrams deserved a home of their own on the web.

The physigram micro-site, part of physicality.org includes descriptions of physical interaction properties, a complete key to the physigram notation, and many examples of physigrams in action from light switches, to complete control panels and novel devices.

Timing matters!

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How long is an instant? The answer, of course, is ‘it depends’, but I’ve been finding it fascinating playing on the demo page for AngularJS tooltips. and seeing what feels like ‘instant’ for a tooltip.

The demo allows you to adjust the md-delay property so you can change the delay between hovering over a button and the tooltip appearing, and then instantly see what that feels like.

Try it yourself, set a time and then either move over the button as if you were about to click t, or wondering what it does, or simply pass over it as if you were moving your pointer to another part of the page.
 
If the delay is too short (e.g. 0), the tooltip flickers as you simply pass over the icon.
 
If you want it as a backup for when someone forgets the action, then something longer about a second is fine – the aim is to be there only if the user has that moment doubt.
 
However, I was fascinated by how long the delay needed to be to feel ‘instant’ and yet not appear by accident.
 
For me about 150 ms is not noticeable as a delay, whereas 200ms I can start to notice – not an annoying delay, but a very slight sense of lack of responsiveness.

Students love digital … don’t they?

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In the ever accelerating rush to digital delivery, is this actually what students want or need?

Last week I was at Talis Insight conference. As with previous years, this is a mix of sessions focused on those using or thinking of using Talis products, with lots of rich experience talks. However, also about half of the time is dedicated to plenaries about the current state and future prospects for technology in higher education; so well worth attending (it is free!) whether or not you are a Talis user.

Speakers this year included Bill Rammell, now Vice-Chancellor at the University of Bedfordshire, but who was also Minister of State for Higher Education during the second Blair government, and during that time responsible for introducing the National Student Survey.

Another high profile speaker was Rosie Jones, who is Director of Library Services at the Open University … which operates somewhat differently from the standard university library!

However, among the VCs, CEOs and directors of this and that, it was the two most junior speakers who stood out for me. Eva Brittin-Snell and Alex Davie are to SAGE student scholars from Sussex. As SAGE scholars they have engaged in research on student experience amongst their peers, speak at events like this and maintain a student blog, which includes, amongst other things the story of how Eva came to buy her first textbook.

Eva and Alex’s talk was entitled “Digital through a student’s eyes” (video). Many of the talks had been about the rise of digital services and especially the eTextbook. Eva and Alex were the ‘digital natives’, so surely this was joy to their ears. Surprisingly not.

Alex, in her first year at university, started by alluding to the previous speakers, the push for book-less libraries, and general digital spiritus mundi, but offered an alternative view. Students were annoyed at being asked to buy books for a course where only a chapter or two would be relevant; they appreciated the convenience of an eBook, when core textbooks were permanently out on and, and instantly recalled once one got hold of them. However, she said they still preferred physical books, as they are far more usable (even if heavy!) than eBooks.

Eva, a fourth year student, offered a different view. “I started like Aly”, she said, and then went on to describe her change of heart. However, it was not a revelation of the pedagogical potential of digital, more that she had learnt to live through the pain. There were clear practical and logistic advantages to eBooks, there when and where you wanted, but she described a life of constant headaches from reading on-screen.

Possibly some of this is due to the current poor state of eBooks that are still mostly simply electronic versions of texts designed for paper. Also, one of their student surveys showed that very few students had eBook readers such as Kindle (evidently now definitely not cool), and used phones primarily for messaging and WhatsApp. The centre of the student’s academic life was definitely the laptop, so eBooks meant hours staring at a laptop screen.

However, it also reflects a growing body of work showing the pedagogic advantages of physical note taking, potential developmental damage of early tablet and smartphone use, and industry figures showing that across all areas eBook sales are dropping and physical book sales increasing. In addition there is evidence that children and teenagers people prefer physical books, and public library use by young people is growing.

It was also interesting that both Alex and Eva complained that eTextbooks were not ‘snappy’ enough. In the age of Tweet-stream presidents and 5-minute attention spans, ‘snappy’ was clearly the students’ term of choice to describe their expectation of digital media. Yet this did not represent a loss of their attention per se, as this was clearly not perceived as a problem with physical books.

… and I am still trying to imagine what a critical study of Aristotle’s Poetics would look like in ‘snappy’ form.

There are two lessons from this for me. First what would a ‘digital first’ textbook look like. Does it have to be ‘snappy’, or are there ways to maintain attention and depth of reading in digital texts?

The second picks up on issues in the co-authored paper I presented at NordiChi last year, “From intertextuality to transphysicality: The changing nature of the book, reader and writer“, which, amongst other things, asked how we might use digital means to augment the physical reading process, offering some of the strengths of eBooks such as the ability to share annotations, but retaining a physical reading experience.  Also maybe some of the physical limitations of availability could be relieved, for example, if university libraries work with bookshops to have student buy and return schemes alongside borrowing?

It would certainly be good if students did not have to learn to live with pain.

We have a challenge.

Of academic communication: overload, homeostatsis and nostalgia

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open-mailbox-silhouetteRevisiting on an old paper on early email use and reflecting on scholarly communication now.

About 30 years ago, I was at a meeting in London and heard a presentation about a study of early email use in Xerox and the Open University. At Xerox the use of email was already part of their normal culture, but it was still new at OU. I’d thought they had done a before and after study of one of the departments, but remembered clearly their conclusions: email acted in addition to other forms of communication (face to face, phone, paper), but did not substitute.

Gilbert-Cockton-from-IDFIt was one of those pieces of work that I could recall, but didn’t have a reference too. Facebook to the rescue! I posted about it and in no time had a series of helpful suggestions including Gilbert Cockton who nailed it, finding the meeting, the “IEE Colloquium on Human Factors in Electronic Mail and Conferencing Systems” (3 Feb 1989) and the precise paper:

Fung , T. O’Shea , S. Bly. Electronic mail viewed as a communications catalyst. IEE Colloquium on Human Factors in Electronic Mail and Conferencing Systems, , pp.1/1–1/3. INSPEC: 3381096 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=197821

In some extraordinary investigative journalism, Gilbert also noted that the first author, Pat Fung, went on to fresh territory after retirement, qualifying as a scuba-diving instructor at the age of 75.

The details of the paper were not exactly as I remembered. Rather than a before and after study, it was a comparison of computing departments at Xerox (mature use of email) and OU’s (email less ingrained, but already well used). Maybe I had simply embroidered the memory over the years, or maybe they presented newer work at the colloquium, than was in the 3 page extended abstract.   In those days this was common as researchers did not feel they needed to milk every last result in a formal ‘publication’. However, the conclusions were just as I remembered:

“An exciting finding is its indication that the use of sophisticated electronic communications media is not seen by users as replacing existing methods of communicating. On the contrary, the use of such media is seen as a way of establishing new interactions and collaboration whilst catalysing the role of more traditional methods of communication.”

As part of this process following various leads by other Facebook friends, I spent some time looking at early CSCW conference proceedings, some at Saul Greenburg’s early CSCW bibliography [1] and Ducheneaut and Watts (15 years on) review of email research [2] in the 2005 HCI special issue on ‘reinventing email’ [3] (both notably missing the Fung et al. paper). I downloaded and skimmed several early papers including Wendy McKay’s lovely early (1988) study [4] that exposed the wide variety of ways in which people used email over and above simple ‘communication’. So much to learn from this work when the field was still fresh,

This all led me to reflect both on the Fung et al. paper, the process of finding it, and the lessons for email and other ‘communication’ media today.

Communication for new purposes

A key finding was that “the use of such media is seen as a way of establishing new interactions and collaboration“. Of course, the authors and their subjects could not have envisaged current social media, but the finding if this paper was exactly an example of this. In 1989 if I had been trying to find a paper, I would have scoured my own filing cabinet and bookshelves, those of my colleagues, and perhaps asked people when I met them. Nowadays I pop the question into Facebook and within minutes the advice starts to appear, and not long after I have a scanned copy of the paper I was after.

Communication as a good thing

In the paper abstract, the authors say that an “exciting finding” of the paper is that “the use of sophisticated electronic communications media is not seen by users as replacing existing methods of communicating.” Within paper, this is phrased even more strongly:

“The majority of subjects (nineteen) also saw no likelihood of a decrease in personal interactions due to an increase in sophisticated technological communications support and many felt that such a shift in communication patterns would be undesirable.”

Effectively, email was seen as potentially damaging if it replaced other more human means of communication, and the good outcome of this report was that this did not appear to be happening (or strictly subjects believed it was not happening).

However, by the mid-1990s, papers discussing ’email overload’ started to appear [5].

I recall a morning radio discussion of email overload about ten years ago. The presenter asked someone else in the studio if they thought this was a problem. Quite un-ironically, they answered, “no, I only spend a couple of hours a day”. I have found my own pattern of email change when I switched from highly structured Eudora (with over 2000 email folders), to Gmail (mail is like a Facebook feed, if it isn’t on the first page it doesn’t exist). I was recently talking to another academic who explained that two years ago he had deliberately taken “email as stream” as a policy to control unmanageable volumes.

If only they had known …

Communication as substitute

While Fung et al.’s respondents reported that they did not foresee a reduction in other forms of non-electronic communication, in fact even in the paper the signs of this shift to digital are evident.

Here are the graphs of communication frequency for the Open University (30 people, more recent use of email) and Xerox (36 people, more established use) respectively.

( from Fung et al., 1989)

( from Fung et al., 1989)

( from Fung et al., 1989)

( from Fung et al., 1989)

It is hard to draw exact comparisons as it appears there may have been a higher overall volume of communication at Xerox (because of email?).  Certainly, at that point, face-to-face communication remains strong at Xerox, but it appears that not only the proportion, but total volume of non-digital non-face-to-face communications is lower than at OU.  That is sub substitution has already happened.

Again, this is obvious nowadays, although the volume of electronic communications would have been untenable in paper (I’ve sometimes imagined printing out a day’s email and trying to cram it in a pigeon-hole), the volume of paper communications has diminished markedly. A report in 2013 for Royal Mail recorded 3-6% pa reduction in letters over recent years and projected a further 4% pa for the foreseeable future [6].

academic communication and national meetungs

However, this also made me think about the IEE Colloquium itself. Back in the late 1980s and 1990s it was common to attend small national or local meetings to meet with others and present work, often early stage, for discussion. In other fields this still happens, but in HCI it has all but disappeared. Maybe I have is a little nostalgia, but this does seem a real loss as it was a great way for new PhD students to present their work and meet with the leaders in their field. Of course, this can happen if you get your CHI paper accepted, but the barriers are higher, particularly for those in smaller and less well-resourced departments.

Some of this is because international travel is cheaper and faster, and so national meetings have reduced in importance – everyone goes to the big global (largely US) conferences. Many years ago research on day-to-day time use suggested that we have a travel ‘time budget’ reactively constant across counties and across different kinds of areas within the same country [7]. The same is clearly true of academic travel time; we have a certain budget and if we travel more internationally then we do correspondingly less nationally.

(from Zahavi, 1979)

(from Zahavi, 1979)

However, I wonder if digital communication also had a part to play. I knew about the Fung et al. paper, even though it was not in the large reviews of CSCW and email, because I had been there. Indeed, the reason that the Fung et al.paper was not cited in relevant reviews would have been because it was in a small venue and only available as paper copy, and only if you know it existed. Indeed, it was presumably also below the digital radar until it was, I assume, scanned by IEE archivists and deposited in IEEE digital library.

However, despite the advantages of this easy access to one another and scholarly communication, I wonder if we have also lost something.

In the 1980s, physical presence and co-presence at an event was crucial for academic communication. Proceedings were paper and precious, I would at least skim read all of the proceedings of any event I had been to, even those of large conferences, because they were rare and because they were available. Reference lists at the end of my papers were shorter than now, but possibly more diverse and more in-depth, as compared to more directed ‘search for the relevant terms’ literature reviews of the digital age.

And looking back at some of those early papers, in days when publish-or-perish was not so extreme, when cardiac failure was not an occupational hazard for academics (except maybe due to the Cambridge sherry allowance), at the way this crucial piece of early research was not dressed up with an extra 6000 words of window dressing to make a ‘high impact’ publication, but simply shared. Were things more fun?

 

[1] Saul Greenberg (1991) “An annotated bibliography of computer supported cooperative work.” ACM SIGCHI Bulletin, 23(3), pp. 29-62. July. Reprinted in Greenberg, S. ed. (1991) “Computer Supported Cooperative Work and Groupware”, pp. 359-413, Academic Press. DOI: http://dx.doi.org/10.1145/126505.126508
https://pdfs.semanticscholar.org/52b4/d0bb76fcd628c00c71e0dfbf511505ae8a30.pdf

[2] Nicolas Ducheneaut and Leon A. Watts (2005). In search of coherence: a review of e-mail research. Hum.-Comput. Interact. 20, 1 (June 2005), 11-48. DOI= 10.1080/07370024.2005.9667360
http://www2.parc.com/csl/members/nicolas/documents/HCIJ-Coherence.pdf

[3] Steve Whittaker, Victoria Bellotti, and Paul Moody (2005). Introduction to this special issue on revisiting and reinventing e-mail. Hum.-Comput. Interact. 20, 1 (June 2005), 1-9.
http://www.tandfonline.com/doi/abs/10.1080/07370024.2005.9667359

[4] Wendy E. Mackay. 1988. More than just a communication system: diversity in the use of electronic mail. In Proceedings of the 1988 ACM conference on Computer-supported cooperative work (CSCW ’88). ACM, New York, NY, USA, 344-353. DOI=http://dx.doi.org/10.1145/62266.62293
https://www.lri.fr/~mackay/pdffiles/TOIS88.Diversity.pdf

[5] Steve Whittaker and Candace Sidner (1996). Email overload: exploring personal information management of email. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’96), Michael J. Tauber (Ed.). ACM, New York, NY, USA, 276-283. DOI=http://dx.doi.org/10.1145/238386.238530
https://www.ischool.utexas.edu/~i385q/readings/Whittaker_Sidner-1996-Email.pdf

[6] The outlook for UK mail volumes to 2023. PwC prepared for Royal Mail Group, 15 July 2013
http://www.royalmailgroup.com/sites/default/files/ The%20outlook%20for%20UK%20mail%20volumes%20to%202023.pdf

[7] Yacov Zahavi (1979). The ‘UMOT’ Project. Prepared For U.S. Department Of Transportation Ministry Of Transport and Fed. Rep. Of Germany.
http://www.surveyarchive.org/Zahavi/UMOT_79.pdf