AI and why we should all be worried - AI's dirty secrets

I gave a talk on AI for the Swindon Science Cafe. Here below are the slides, followed by my notes.

Slide 1

Hi, my name is Alan Winfield. Thank you Rod and Claire for inviting me to speak this evening.

Slide 2

So, what does a robot and AI ethicist do? Well, the short answer is worry.

I worry about the ethical and societal impact of AI on individuals, society and the environment. Here are some keywords on ethics, reflecting that we must work toward AI that respects Human Rights, diversity and dignity, is unbiased and sustainable, transparent, accountable and socially responsible. I also work in Standards with both the British Standards Institute and the International IEEE Standards Association.

Slide 3

Before getting into the ethics of AI I need to give you a quick tutorial on machine learning.

The most powerful and exciting AI today is based on Artificial Neural Networks (ANNs). Here is a simplified diagram of a Deep Learning network for recognizing images. Each small circle is a *very* simplified mathematical model of biological neurons, and the outputs of each layer of artificial neurons feed the inputs of the next layer. In order to be able to recognise images the network must first be trained with images that are already labelled - in this case my dear late dog Lola.

But in order to reliably recognise Lola the network needs to be trained not with one picture of Lola but many. This set of images is called the training data set and without a good data set the network will not work at all or will be biased. (In reality there will need to be not 4 but hundreds of images of Lola).

But even a simple ANN can get things wrong. A famous example was an ANN like this trained on pictures of wolves. After training they input of a bear, but the network identified it as a wolf. Why? Because all of the wolf pictures had snowy backgrounds, so the network learned to recognize snow, not a wolf. The bear also had a snowy background.

This is one example of what we now call a ‘hallucination’ in big AIs.

Slide 4

We’ve been worrying about the existential threat of AI for a long time: here is a piece I wrote for the Guardian in 2014, when ‘the singularity was the main thing we worried about.

The singularity is the idea that as soon as AI is smarter than humans then the AIs will rapidly improve themselves, with unforeseeable consequences for human civilization.

But the singularity is a thing for the techno-utopians: wealthy middle-aged men who regard the singularity as their best chance of immortality. They are Singularitarians, some of whom appear prepared to go to extremes to stay alive for long enough to benefit from a benevolent super-AI - a manmade god that grants transcendence.

And it's a Thing for the doomsayers, the techno-dystopians. Apocalypsarians who are equally convinced that a superintelligent AI will have no interest in curing cancer or old age, or ending poverty, but will instead - malevolently or maybe just accidentally - bring about the end of human civilisation as we know it. History and Hollywood are on their side. From the Golem to Frankenstein's monster, Skynet and the Matrix, we are fascinated by the old story: man plays god and then things go horribly wrong.

Slide 5

Today we have influential scientists who worry about Artificial General Intelligence (AGI). Notable among these is physicist and cosmologist Max Tegmark.

At the AI safety summit in February 2025 Tegmark argued that we need a middle pathway between No AI and Uncontrollable AGI, which he calls guaranteed safe tool AI. He suggested a policy solution in which the US and China both launch national safety standards preventing their own AI companies from building AGI. Leading to what Tegmark rather optimistically calls ‘an age of unprecendented global prosperity powered by safe tool AI.

Is there an existential threat from AI itself? No. I fear human stupidity much more than artificial intelligence.

So should we be worried? Yes. But the things I worry about are rather more down to earth.

In the rest of this talk I will consider the energy costs of AI, then the human costs.

Slide 6

In 2016 Go champion Lee Sedol was famously defeated by DeepMind's AlphaGo. It was a remarkable achievement for AI. But consider the energy cost. In a single two-hour match Sedol burned around 170 kcals: roughly the amount of energy you would get from an egg sandwich. Or about 1 Watt – the power of an LED night light.

In the same two hours the AlphaGo machine reportedly consumed about 50,000 Watts. The same as a 50 kW generator for industrial lighting. And that's not taking account of the energy used to train AlphaGo.

Slide 7

A paper published in 2019 paper revealed, for the first time, estimates of the carbon cost of training large AI models for natural language processing such as machine translation. The carbon cost of simple models is quite modest, but with tuning and experimentation the carbon cost leaps to 7 times the carbon footprint of an average human in one year (or 2 times if you're an American).

And the energy cost of optimizing the biggest model is a staggering 5 times the carbon cost of a car over its whole lifetime, including manufacturing it in the first place. The dollar cost of that amount of energy is estimated at between one and 3 million US$. (Something that only companies with very deep pockets can afford.)

These energy costs seem completely at odds with the urgent need to meet sustainable development goals. At the very least AI companies need to be honest about the huge energy costs of machine learning.

Slide 8

At the same Paris meeting earlier this year AI ethicist Kate Crawford drew attention to both energy and water costs of AI. Crawford predicts that the energy cost of training generative AIs will soon overtake the total energy consumption of industrialized nations such as Japan.

She also drew attention to the colossal amounts of clean water that AI server farms need to keep them cool. Water that is already a scarce resource.

One study estimated that ChatGPT-3 requires 700,000 litres of clean water for training, And that each user interaction costs around half a litre of water.

Source: https://interestingengineering.com/innovation/training-chatgpt-consumes-water

Slide 9

The very same Kate Crawford, together with a colleague, produced this extraordinary map of the entire process behind the Amazon Echo.

The remarkable Anatomy of an AI System, shows The Amazon Echo as an anatomical map of human labour, data and planetary resources. 

The map is far too detailed to see on this slide but let me just zoom in on the top of this very large iceberg where we find the amazon echo and its human user, shown here in a yellow dashed box.

Slide 10

At the very top of this pyramid of materials and energy (on the left) and waste (on the right) is you – the user of the Amazon Echo – and your unpaid human labour providing habits and preferences that will be used as training data. 

I strongly recommend you check this out. It is truly eye opening.

Slide 11

Now I want to turn to the human cost of AI.

It is often said that one of the biggest fears around AI is the loss of jobs. In fact the opposite is happening. Many new jobs are being created, but the tragedy is that they are not great jobs, to say the least. Let me introduce you to 3 of these new kinds of jobs.

Conversational AI or chat bots also need human help. Amazon for instance employs thousands of both full-time employees and contract workers to listen to and annotate speech. The tagged speech is then fed back to Alexa to improve its comprehension. In 2019 the Guardian reported that Google employs around 100,000 temps, vendors and contractors: literally an army of linguists to create the handcrafted data sets required for Google translate to learn dozens of languages. Not surprisingly there is a considerable disparity between the wages and working conditions of these workers and Google's full-time employees.

AI tagging jobs are dull, repetitive and in the case of the linguists highly skilled.

Slide 12

Consider AI tagging of images. This is the manual labelling of objects in images to, for instance, generate training data sets for driverless car AIs. Better (and safer) AI needs huge training data sets and a whole new outsourced industry has sprung up all over the world to meet this need. Here is an AI tagging factory in China.

Slide 13

But by far the worst kind of new white-collar job in the AI industry is content moderation.

These tens of thousands of people, employed by third-party contractors, are required to watch and vet offensive content: hate speech, violent pornography, cruelty and sometimes murder of both animals and humans for Facebook, YouTube and other media platforms. These jobs are not just dull and repetitive they are positively dangerous. Harrowing reports tell of PTSD-like trauma symptoms, panic attacks and burn out after one year, alongside micromanagement, poor working conditions and ineffective counselling. And very poor pay - typically $28,800 a year. Compare this with average annual salaries at Facebook of $250,000+.

Slide 14

The extent to which AI has a human supply chain was a big revelation, and I am an AI insider! The genius designers of this amazing tech rely on both huge amounts of energy and a hidden army of what Mary Gray and Siddhartha Suri call Ghost Workers.

I would ask you to consider the question: how can we, as ethical consumers, justify continuing to make use of unsustainable and unethical AI technologies?

Slide 15  

AI ethics are important because AIs are already causing harm. Actually a *very* wide range of harms.

Fortunately, there is an excellent crowdsourced database which collects reports of accidents and near misses involving robots (including autonomous vehicles) and AIs.

The AI incidents database covers both accidents and near misses. I strongly recommend you check it out.

It is important to note that because the AI incidents database is crowdsourced from accidents and near misses that made it into the press and media, what we see is almost certainly only the tip of the iceberg of the harms being done by AI.

The database contains some truly shocking cases. One is a 14-year-old boy, who died by suicide after reportedly becoming dependent on Character.ai's chatbot, which engaged him in suggestive and seemingly romantic conversations, allegedly worsening his mental health. Source: Can A.I. Be Blamed for a Teen’s Suicide? New York Times, Oct 2024.

The database also highlights the criminal use of AI. Examples include criminals phoning parents claiming they have kidnapped a child and demanding a ransom, with deepfake audio of the teenage girl audible in the background. And there are many examples of sextortion, using deepfake AI generated video of famous individuals engaged in sex acts.

The database really highlights the sad truth that AI is a gift to criminals.

Slide 16  

Another more recent database tracks the misuse of AI by lawyers when preparing court cases. The database only shows those instances where the judge (or another officer in the court) spotted the hallucinated decisions, citations or quotations. The cases were thrown out, and in some cases the lawyers found to be using Ai were fined or reported to their bar associations.

While this is not criminal misuse of AI, it does demonstrate a lack of understanding of AI, or at best, naivety. Perhaps the real culprits are hard pressed paralegals. This database underlines the need for professionals to be property trained in AI and its weaknesses.

Since I grabbed this screen shot the number if cases reported has grown.

Slide 17

Lawyer Graziano Mioli elegantly argues that we have a categorical imperative to be imperative when interacting with AIs. Noting that this is not an invitation to be rude.

For Graziano's slides see https://www.youtube.com/watch?v=tjBnGN4u1GA&ab_channel=GrazianoMioli  

I can see why a majority of people interact kindly with AIs. I think it reflects well on those who do say ‘please’, for the Kantian reason that we should not get into the habit of acting unkindly. (Kant famously argued that you should not kick a dog - not for animal welfare reasons, but because you might then be more inclined to kick a human).

See https://www.theaihunter.com/news/ai-etiquette-why-some-people-say-please-to-chatbots/

Slide 18

Having mostly elaborated on the dangers of AI, I want to finish on a positive.

We already enjoy the benefit of useful and reliable AI technology, like smart maps and machine translation. DeepMind's diagnostic AI can detect over 50 eye diseases from retinal scans as accurately as a doctor, and DeepScribe provides automated note taking during a consultation, linking with patient electronic health records

Thank you! 

 

This talk is an extended and updated version of a short talk I gave in June 2019.

The Energy Cost of Online Living in Lockdown

Readers of his blog will know that one of the many things ethical I worry about is the energy cost of AI. As part of the work I'm doing with Claudia Pagliari and her National Expert Group on Digital Ethics for Scotland I've been looking also into the energy costs of what is - for many of us - everyday digital life in lockdown. I don't yet have a complete set of results but what I have found so far is surprising - and not in a good way.

So far I've looked into the energy costs of (i) uploading to the cloud, (ii) streaming video (i.e. from iPlayer or Netflix), and (iii) video conferencing.

(i) Uploading to the cloud. This 2017 article in the Stanford Magazine explains that when you save a 1 Gbyte file – that’s about 1 hour of video - to your laptop’s disk drive the energy cost is 0.000005 kWh, or 5 milliWatt hours. Save the same file to the Cloud and the energy cost is between 3 and 7 kWh. For comparison your electric kettle burns about 3 kWh. This mean that the energy cost of saving to the cloud is about a million times higher than to your local disk drive. 

The huge difference makes sense when you consider that there is a very complex international network of switches, routers and exchange hubs, plus countless amplifiers maintaining signal strength over long distance transmission lines. All of this consumes energy. Then add a slice of the energy costs of the server farm.

(ii) Streaming video. This article in The Times from May 2019 makes the claim that streaming a 2 hour HD movie from Netflix incurs the same energy cost as boiling 10 kettles (based on the sustainable computing research of Mike Hazas). To estimate  how much energy that equates to we need to guess how full the kettle is. A half full 3kWh kettle will take about 2 minutes to boil, and consume therefore 100 Watts. Do that 10 times and you've burned 1kW. A DVD player typically consumes 8 Watts, so streaming costs 125 times more energy.

Again this makes sense against uploading to the cloud, except that here you are downloading from Netflix servers. A 2 hour HD movie is alot of data, around 10GBytes, so 10 times more than the case for (i) above.

(iii) Video conferencing. This post on David Mytton's excellent blog explores the energy cost of Zoom meetings in some detail. David estimates that a 1 hour video zoom call with 6 participants generates between 5 and 15GB of data and that the data transfer consumes between 0.07 – 0.22kWh of electricity. Using our benchmark of kettles boiled this is pretty modest - at most less than one tenth of the energy cost. 

However this estimate makes 2 assumptions: first that you are connected via cable or fixed line - which here in the UK costs 0.015kWh per GByte. A mobile connection costs about seven times that at 0.1kWh/GB. And second, this estimate measures only the energy costs of data transmission and fails to take account of the energy costs of Zoom's data centres, which - if (i) and (ii) here are anything to go by, could be significant, especially since there aren't any in the UK and the default servers are in the US.

As this article on the Zoom blog explains, Zoom calls are not peer to peer. The video from each participant is streamed first to a zoom server then broadcast to every other person on the call. As David Mytton says Zoom don't release information on the overall energy costs of calls. I strongly suspect that if server energy costs were factored in they would be in line with cases (i) and (ii) above. Even so, I feel sure that David Mytton's overall conclusion remains true: that the energy cost of Zoom meetings is significantly lower than all but local or regional travel.

 

I would like to see networking services like cloud storage, video on demand and video conferencing publish a meaningful energy cost. When we buy packaged food from the supermarket we expect to read the calorific energy value of each item, broken down into fat, salt and so on.  It would be great if every online transaction, from sending an email, to watching a movie revealed its energy/carbon cost. Not just for energy geeks like me, but to remind all of us that the Digital Economy is *very* energy hungry.

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I would welcome any additional data which either adds to the above (especially the energy costs for smaller online transactions like tweets, emails or card payments), or shows that the estimates above are wrong. 

Related blog posts:

On Sustainable Robotics
Energy and Exploitation: AIs dirty secrets
What's wrong with Consumer Electronics? 

 

On Sustainable Robotics

The climate emergency brooks no compromise: every human activity or artefact is either part of the solution or it is part of the problem. 

I've worried about the sustainability of consumer electronics for some time, and, more recently, the shocking energy costs of big AI. But the climate emergency has also caused me to think hard about the sustainability of robots. In recent papers we have defined responsible robotics as

... the application of Responsible Innovation in the design, manufacture, operation, repair and end-of-life recycling of robots, that seeks the most benefit to society and the least harm to the environment.

I will wager that few robotics manufacturers - even the most responsible - pay much attention to repairability and environmental impact. And, I'm ashamed to say, very little robotics research is focused on the development of sustainable robots. A search on google scholar throws up just a handful of great papers detailing work on upcycled and sustainable robots (2018), sustainable robotics for smart cities (2018), and sustainable soft robots (2020).

I was then delighted when, a few weeks ago, my friend and colleague Michael Fisher, drafted a proposal for a new standard on Sustainable Robotics. The proposal received strong support from the BSI robotics committee. Here is the formal notice requesting comments on Michael's proposal: BS XXXX Guide to the Sustainable Design and Application of Robotic Systems. Anyone can comment (although you do need to register first). The deadline is 1 April 2021. 

So what would make a robot sustainable? In my view it would have to be:

1. Made from sustainable materials. This means the robot should, as far as possible, use recycled materials (plastics or metals), or biodegradable materials like wood. Any new materials should be ethically sourced. 
2. Low energy. The robot should be designed to use as little energy as possible. It should have energy saving modes. If an outdoor robot then it should use solar cells and/or hydrogen cells when they become small enough for mobile robots. Battery powered robots should always be rechargeable. 
3. Repairable. The robot would be designed for ease of repair, using modular, replaceable parts as much as possible - especially the battery. Additionally the manufacturers should provide a repair manual so that local workshops could fix most faults. 
4. Recyclable. Robots will eventually come to the end of their useful life, and if they cannot be repaired or recycled we risk them being dumped in landfill. To reduce this risk the robot should be designed to make it easy to re-use parts, such as electronics and motors, and re-cycle batteries, metals and plastics.
   

These are, for me, the four fundamental requirements, but there are others. The BSI proposal adds the environmental effects of deployment (it is unlikely we would consider a sustainable robot designed to spray pesticides as truly sustainable), or of failure in the field. Also the environmental effect of maintenance; cleaning materials, for instance. The proposal also looks toward sustainable, upcyclable robots as part of a circular economy.

This is Ecobot III, developed some years ago by colleagues in the Bristol Robotics Lab's Bio-energy group. The robot runs on electricity extracted from biomass by 48 microbial fuel cells (the two concentric brick coloured rings). The robot is 90% 3D printed, and the plastic is recyclable.

 

 

 

 

 

I would love to see, in the near term, not only a new standard on Sustainable Robotics as a guide (and spur) for manufacturers, but the emergence of Sustainable Robotics as a thriving new sub-discipline in robotics.

Energy and Exploitation: AIs dirty secrets

A couple of days ago I gave a short 15 minute talk at an excellent 5x15 event in Bristol. The talk I actually gave was different to the one I'd originally suggested. Two things prompted the switch: one was seeing the amazing line up of speakers on the programme - all covering more or less controversial topics - and the other was my increasing anger in recent months over the energy and human costs of AI. So it was that I wrote a completely new talk the day before this event.

But before I get to my talk I must mention the amazing other speakers: we heard Phillipa Perry speaking on child parent relationships, Hallie Rubenhold on the truth about Jack the Ripper's victims,  Jenny Riley speaking very movingly about One25's support for Bristol's (often homeless) sex workers, and Amy Sinclair introducing her activism with Extinction Rebellion.

Here is the script for my talk (for the slides go to the end of this blog post).

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Artificial Intelligence and Machine Learning are often presented as bright clean new technologies with the potential to solve many of humanity's most pressing problems.

We already enjoy the benefit of truly remarkable AI technology, like machine translation and smart maps. Driverless cars might help us get around before too long, and DeepMind's diagnostic AI can detect eye diseases from retinal scans as accurately as a doctor.

Before getting into the ethics of AI I need to give you a quick tutorial on machine learning. The most powerful and exciting AI today is based on Artificial Neural Networks. Here [slide 3] is a simplified diagram of a Deep Learning network for recognizing images. Each small circle is a *very* simplified mathematical model of biological neurons, and the outputs of each layer of artificial neurons feed the inputs of the next layer. In order to be able to recognise images the network must first be trained with images that are already labelled - in this case my dog Lola.

But in order to reliably recognise Lola the network needs to be trained not with one picture of Lola but many. This set of images is called the training data set and without a good data set the network will not work at all or will be biased. (In reality there will need to be not 4 but hundreds of images of Lola).

So what does an AI ethicist do? Well, the short answer is worry. I worry about the ethical and societal impact of AI on individuals, society and the environment. Here are some keywords on ethics [slide 4], reflecting that we must work toward AI that respects Human Rights, diversity and dignity, is unbiased and sustainable, transparent, accountable and socially responsible.

But I do more than just worry. I also take practical steps like drafting ethical principles, and helping to write ethical standards for the British Standards Institute and the IEEE Standards Association. I lead P7001: a new standard on transparency in of autonomous systems based on the simple ethical principle that it should always be possible to find out why an AI made a particular decision. I have given evidence in parliament several times, and recently took part in a study of AI and robotics in healthcare and what this means for the workforce of the NHS.

Now I want to share two serious new worries with you.

The first is about the energy cost of AI. In 2016 Go champion Lee Sedol was famously defeated by DeepMind's AlphaGo. It was a remarkable achievement for AI. But consider the energy cost. In a single two hour match Sedol burned around 170 kcals: roughly the amount of energy you would get from an egg sandwich.  Or about the power of an LED night light -  1 Watt. In the same two hours the AlphaGo machine reportedly consumed 50,000 times more energy than Sedol. Equivalent to a 50 kW generator for industrial lighting. And that's not taking account of the energy used to train AlphaGo.

Now some people think we can make human equivalent AI by simulating the human brain. But the most complex animal brain so far simulated is that of c-elegans – the nematode worm. It has 302 neurons and about 5000 synapses - these are the connections between neurons. A couple of years ago I worked out that simulating a neural network for a simple robot with only a 10th the number of neurons of c-elegans costs 2000 times more energy than the whole worm.

In a new paper that came out just a few days ago we have for the first time estimates of the carbon cost of training large AI models for natural language processing such as machine translation [1]. The carbon cost of simple models is quite modest, but with tuning and experimentation the carbon cost leaps to 7 times the carbon footprint of an average human in one year (or 2 times if you're an American).

And the energy cost of optimising the biggest model is a staggering 5 times the carbon cost of a car over its whole lifetime, including manufacturing it in the first place. The dollar cost of that amount of energy is estimated at between one and 3 million US$. (Something that only companies with very deep pockets can afford.)

These energy costs seem completely at odds with the urgent need to halve carbon dioxide emissions by 2030. At the very least AI companies need to be honest about the huge energy costs of machine learning.

Now I want to turn to the human cost of AI. It is often said that one of the biggest fears around AI is the loss of jobs. In fact the opposite is happening. Many new jobs are being created, but the tragedy is that they are not great jobs, to say the least. Let me introduce you to two of these new kinds of jobs.

The first is AI tagging. This is manually labelling objects in images to, for instance, generate training data sets for driverless car AIs. Better (and safer) AI needs huge training data sets and a whole new outsourced industry has sprung up all over the world to meet this need. Here [slide 9] is an AI tagging factory in China.

Conversational AI or chat bots also need human help. Amazon for instance employs thousands of both full-time employees and contract workers to listen to and annotate speech. The tagged speech is then fed back to Alexa to improve its comprehension. And last month the Guardian reported that Google employs around 100,000 temps, vendors and contractors: literally an army of linguists working in "white collar sweatshops" to create the handcrafted data sets required for Google translate to learn dozens of languages. Not surprisingly there is a huge disparity between the wages and working conditions of these workers and Google's full time employees.

AI tagging jobs are dull, repetitive and in the case of the linguists highly skilled. But by far the worst kind of new white collar job in the AI industry is content moderation.

These tens of thousands of people, employed by third-party contractors, are required to watch and vet offensive content: hate speech, violent pornography, cruelty and sometimes murder of both animals and humans for Facebook, YouTube and other media platforms [2]. These jobs are not just dull and repetitive they are positively dangerous. Harrowing reports tell of PTSD-like trauma symptoms, panic attacks and burnout after one year, alongside micromanagement, poor working conditions and ineffective counselling. And very poor pay - typically $28,800 a year. Compare this with average annual salaries at Facebook of ~$240,000.

The big revelation to me over the past few months is the extent to which AI has a human supply chain, and I am an AI insider! The genius designers of this amazing tech rely on both huge amounts of energy and a hidden army of what Mary Gray and Siddhartha Suri call Ghost Workers.

I would like to leave you with a question: how can we, as ethical consumers, justify continuing to make use of unsustainable and unethical AI technologies?

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References:

[1] Emma Strubell, Ananya Ganesh, Andrew McCallum (2019) Energy and Policy Considerations for Deep Learning in NLP, arXiv:1906.02243
[2] Sarah Roberts (2016) Digital Refuse: Canadian Garbage, Commercial Content Moderation and the Global Circulation of Social Media’s Waste, Media Studies Publications. 14.

What's wrong with Consumer Electronics?

When I was a boy the term consumer electronics didn't exist. Then the sum total of household electronics was a wireless, a radiogram and a telephone; pretty much everyone had a wireless, fewer a radiogram and on our (lower middle-class) street perhaps one in five houses had a telephone. (In an emergency it was normal to go round to the neighbour with the phone.) In the whole of my childhood we only ever had the same wireless set and gramophone and both looked more like furniture than electronics, housed in handsome polished wooden cabinets. Of course it was their inner workings, with the warm yellow glow of the thermionic valves that fascinated me and got me into trouble when I took them to pieces, that led to my chosen career in electronics.

How things have changed. Now most middle-class households have more computing power than existed in the world 50 years ago. Multiple TVs, mobile phones, computing devices (laptops, games consoles, iPads, Kindles and the like) and the supporting infrastructure of wireless routers, printer, and backup storage, are now normal. And most of this stuff will be less than five years old. If you're anything like me the Hi-Fi system will be the oldest bit of kit you own (unless you ditched it for the iPod and docking station). Of course this gear is wonderful. I often find myself shocked by the awesomeness of everyday technology. And understanding how it all works only serves to deepen my sense of awe. But, I'm also profoundly worried - and offended too - by the way we consume our electronics.

What offends me is this: modern solid-state electronics is unbelievably reliable - what's wrong with consumer electronics is nothing, yet we treat this magical stuff - fashioned of glass - as stuff to be consumed then thrown away. Think about the last time you replaced a gadget because the old one had worn out or become unrepairable. Hard isn't it. If you still possessed it the mobile phone you had 15 years ago would - I'd wager - still work perfectly. I have a cupboard here at home with all manner of obsolete kit. A dial-up modem for instance, circa 1993. It still works fine - but there's nothing to dial into. The fact is that we are compelled to replace perfectly good nearly-new electronics with the latest model either because the old stuff is rendered obsolete (because it's no longer compatible with current generation o/s, or applications or infrastructure - or unsupported), or worse still because the latest kit has 'must have' features or capabilities not present on the old.

I would like to see a shift in consumer electronics back to a model in which gadgets are designed to be repaired and consumers are encouraged to replace or upgrade every ten years or more, not every year. What I'm suggesting is of course exactly the opposite of what's happening now. Current devices are becoming less repairable, with batteries you can't replace and designs that even skilled technicians find difficult to take apart without risk of damage. The lastest iPad for example was given a very low repairability score (2/10) by iFixit.

And the business model most electronics companies operate is fixated on the assumption that profit, and growth, can only be achieved through very short product life cycles. But all of our stuff is not like this. We don't treat our houses, or gardens, or dining room tables, or central heating systems, or any number of things as consumer goods, but the companies that build and sell houses, or dining room tables, or landscape gardens, etc, still turn a profit. Why can't electronics companies find a business model that treats electronic devices more like houses and less like breakfast cereal?

I don't think consumer electronics should be consumed at all.

Dilemmas of an ethical consumer

I have a dilemma and it is this. I'm torn between lusting after an iPad 2 and serious worries over the ethics of its manufacture.

There's no doubt that the iPad is a remarkable device (Jobs' hyperbole about magical and revolutionary is quite unnecessary). Several academic friends have told me that the iPad and one application in particular - called iAnnotate - has changed their working lives. Having seen them demonstrate iAnnotate there's no doubt it's the academic's killer iPad app. You see, something we have to do all the time is read, review and edit papers, book chapters, grant applications and working documents. For me that normally means printing a paper out, writing all over it, then either tediously scanning the marked up pages - uploading them to Google docs - then emailing the link, or constructing a large email with a list of all my changes and comments. What my friends showed me was them reviewing a paper on the iPad, writing all over it with a stylus, then just emailing back the marked up document. Amazing - this could save me hours every week.

But here's the problem. The iPad may well be a marvel of design and technology but - like most high tech stuff these days - it's profoundly unsustainable and it's manufacture is ethically questionable. Now to be fair to Apple, this is not a problem that's unique to them - and I'm prepared to believe that Apple does genuinely care about the conditions under which it's products are manufactured and is doing all it can to pressure it's subcontractors to provide the best working conditions for their employees. But the problem is systemic - the only reason that we can buy an iPad, or laptop, or flat screen TV, or any number of consumer electronics products for a few hundred pounds is that they're manufactured in developing countries where labour is cheap and working conditions are a million miles from what we would regard as acceptable. And I'm not even going to start here about the sustainability of those products - in terms of the true energy costs, and costs to the environment, of their manufacture during incredibly complex supply chains, or the environmental costs of their disposal after we've finished with them.

This may sound odd given that I'm a professional electronics engineer and elder-nerd. But I'm a late adopter of new technology. Always have been. (My excuse is that I was an early adopter of the transistor.) I also keep stuff for a very long time. My Hi-Fi system is 25 years old and is working just fine. My car is now 6 years old and I fully expect to run it for another 10 years - a modern well-built and maintained car can easily last for 250,000 miles. The most recent high tech thing I bought was a new electric piano. It replaced my old one, bought in 1983, which had become unplayable because the mechanics of the keys had worn out and I fully expect to keep my beautiful new Roland piano for 25 years. My MacBook pro (yes I do like Apple stuff) is now 5 years old and works just fine - not bad for something that's probably had 10,000 hours use. In short I aim to practice what's sometimes called Bangernomics - except I try and apply the philosophy to everything, not just cars. (I'm not exactly a model consumer.)

Maybe that's part of the answer to my dilemma - get an iPad and run it for 20 years..? But even applying Bangernomics still won't salve my conscience when it comes to the ethics or sustainability of its manufacture. So, what am I to do?