AI and ethics - it's not as good as you think and not as bad as you fear

Joanna Bryson, one of the world’s experts in AI and ethics is right when she points out that the big problem in AI and Ethics is ‘anthropomorphising’. AI is competence without comprehension. It can beat you at chess, Go and poker but doesn’t know it has won. Literally hundreds of AI and ethics groups have sprung up over the last couple of years. Some are serious international bodies like the EU, IEEE and so on, but it is important to examine but remain level-headed on this issue. The danger is that we destroy the social goods that AI offer, by demonising it  before it has been tried.

Having just launched a new book ‘AI for Learning’ in which I tackle these ethical issues in some detail, I thought I’d provide a taster for the ethical concerns as they may affect the world of learning. 

Existential

Let’s get one moral issue out of the way – the existential threat. This often centres around Ray Kurzweil's ‘Singularity’, the idea that AI will at one point transcend human intelligence and become uncontrollable. Other AI experts like Stuart Russell, Brett Frischmann and Nick Bostrom have speculated at length on ways in which runaway AI could be a threat to our species. Although there are possible scenarios where runaway AI will lead to our demise as a species, this is not an issue that should worry us much in using AI for learning. Many, such as Stephen Pinker, Daniel Dennett and other serious researchers in AI are sceptical of these end-of-days theories. In any case, it is highly unlikely that AI for education will do much other than protect us from such scenarios.

Bias

Much more relevant is the topic of ‘bias’. The problem with many of the discussions around bias in AI, is that the discussions themselves are loaded with biases; confirmation bias, negativity bias, immediacy bias and so on. Remember that AI is ‘competence without comprehension’ competences that can be changed, whereas all humans have cognitive biases, which are difficult to change. AI is just maths, software and data. This is mathematical bias, for which there are definitions. It is easy to anthropomorphize these problems by seeing one form of bias as the same as the other. That aside, mathematical bias can be built into algorithms and data sets. What the science of statistics, and therefore AI, does, is quantify and try to eliminate such biases. This is, essentially, a design problem, and I don’t see much of a problem in the learning game, where datasets tend to be quite small, for example in adaptive learning. It gets to be a greater problem when using a model such as GPT-3 for learning, where the data set is massive. It can literally produce essay-like content at the click of a button. Nevertheless, I think that the ability of AI to be blind to gender, race, sexuality and social class may, in learning, make it less biased than humans. We need to be careful when I comes to making decisions that humans often make, but at the level of learning engagement, support there’s lots of low hanging fruit that need be of little ethical concern.

Race

The most valuable companies in the world are AI companies, in that their core strategic technology is AI. As to the common charge that AI is largely written by white coders, I can only respond by saying that the total number of white AI coders is massively outgunned by Chinese, Asian and Indian coders. The CEOs of Microsoft and Alphabet (Google) were both born and educated in India. And the CEOs of the three top Chinese tech companies are Chinese. Having spent some time in Silicon Valley last year, it is one of the most diverse working environment I’ve seen in terms of race. We can always do better but this should, in my view not be seen as a crippling ethical issue.

Gender

Gender is an altogether different issue and a much more intractable problem. There seems to be bias in the educational system among parents, teachers and others to steer girls away from STEM subjects and computer studies. But the idea that all algorithms are gender-biased is naïve. If such bias does arise one can work to eliminate the bias. Eliminating human gender bias is much more difficult.

Transparency

It is true that some AI is not wholly transparent, especially deep learning using neural networks. However, we shouldn’t throw out the baby with the bathwater… and the bath. We all use Google and academics use Google Scholar, because they are reliably useful. They are not transparent. The problems arise when AI is used to say, select or assess students. Here, we must ensure that we use systems that are fair. A lot of work is going into technology that interprets other AI software and reveals their inner workings.

Dehumanisation

A danger expressed by some educators is that AI may automate and therefore dehumanise the process of learning. This is often within discussions of robot teachers. I discuss the fallacy of robot teachers in the book. It is largely a silly idea, as silly as having a robot driver in a self-driving car. It is literally getting the wrong end of the stick, as AI in learning is largely about support for learners. Far from dehumanising learning it may empower learners.

Employment

The impact of AI on employment is a lively political and economic topic. Yet, before Covid, we had record levels of employment in the US, UK and China. There seems to be a fair amount of scaremongering at learning conferences, where you commonly see completely fictional quotes, such as ‘65% of children entering primary school today will be doing jobs that have yet to exist’. Even academic studies tend to be hyperbolic, such as the Frey and Osborne (2013) report from Oxford University that claimed ‘47% of jobs will be automated in the next two decades’. Seven years in and the evidence that this is true is slim. What is clear is that skills in creating and using AI for learning will be necessary. Indeed, Covid has accelerated this process. I categorise and list these new skills in the book.

Conclusion

I touch upon all of these issues in the book and stick to my original premise that AI is ‘not as good as you think it is and not as bad as you fear’. Sure there are ethical issues, but these are similar to general ethical issues in software and any area of human endeavour where technology is used. It is important not to see AI as separate from software and technology in general. That’s why I’m on the side of Pinker and Dennett in saying these are manageable problems. We can use technology to police technology. Indeed AI is used to stop sexist, racist and hate text and imagery from appearing online. Technology is always a balance between good and bad. We drive cars despite the fact that 1.3 million people die horrible deaths every year from crashes and many more have serious injuries. Let’s not demonise AI to such a degree that its benefits are not realised and , as I discuss in the book, in education and training the benefits are considerable.

 

AI for Learning

The book ‘AI for Learning’ is available on Amazon. In addition to ethics it covers many facets of AI for learning; teaching, learning, learning support, content creation, chatbots, learning analytics, sentiment analysis, and assessment.

 

Bibliography

Bryson, J.J., Diamantis, M.E. and Grant, T.D., 2017. Of, for, and by the people: the legal lacuna of synthetic persons. Artificial Intelligence and Law, 25(3), pp.273-291.

Kurzweil, R., 2005. The singularity is near: When humans transcend biology. Penguin.

Russell, S., 2019. Human compatible: Artificial intelligence and the problem of control. Penguin.

Clark, D., Review of Human Compatible https://donaldclarkplanb.blogspot.com/search?q=Human+Compatible+by+Stuart+Russell+-+go+to+guy+on+AI+-+a+must+read..

Frischmann, B. and Selinger, E., 2018. Re-engineering humanity. Cambridge University Press.

Clark, D., Review of Re-engineering humanity

https://donaldclarkplanb.blogspot.com/search?q=Frischmann

Bostrom, N., 2017. Superintelligence. Dunod.

Pinker, S., 2018. Enlightenment now: The case for reason, science, humanism, and progress. Penguin.

Dennett, D.C., 2017. From bacteria to Bach and back: The evolution of minds. WW Norton & Company.

Clark, D., Review of From bacteria to Bach and back

https://donaldclarkplanb.blogspot.com/search?q=Dennett+-+why+we+need+polymaths+in+the+AI+ethics+debate

Re-engineering Humanity by Brett Frischmann and Evan Seliger - slippery slope argument that tech steals into our lives with 'cheap bliss'......

Mildly dystopian critique of technology and AI that suggests that we are on a slippery slope, as technology steals into our lives, resulting not in enlightenment but ‘cheap bliss’ and a loss of control and judgement. It is not that we are creating robots but that we are becoming more robotic through what they call techno-engineering.

Like Postman in Amusing Ourselves to Death, and McLuhan, the authors claim that we are being lulled into outsourcing to technology allowing its allure to trap us into its patterns, not ones we freely choose, but patterns of surveillance and obedience. This gradual creep of technology destroys our humanity as contemporary technology invades our minds at three levels; micro- fitness trackers, meso- smart transport and macro- Facebook.

Nicholas Carr in the Foreward, describes the book as a ‘balanced examination…’ but I'm not convinced. It is singular in intent and displays the anti-corporate line that is common in academia. The tech companies are the ‘Frightful Five’ and Taylorism is critiqued to death but, in the end, a bit of a straw man. True, we fetishize technology, or at least the devices, but the idea that Taylorism and Fordism (those old canards) make us impotent puppets is a stretch. Learning faster, saving time and productivity seem like admirable aims to me. There are plenty of administrative and repetitive tasks, student support and marking in education for example, that could do with a dose of efficiency using AI.

Surveillance creep is weakly argued through examples like Fitbits and tracking kids at school, where the benefits seem to be ignored in favour of a position on privacy that few would get worked up about. That aside, it perks up on the dangers of passivity, decreased agency, decreased responsibility, increased ignorance and detachment. Drone parents and brain sensors test moral boundaries, although the usual argument that GPS weakens cognitive control is now a bit tired. 

The Chapter on extended mind theory; extended body, extended cognition, distributed cognition and cognitive technology is informative. Boden, Chalmers, Clark are all explained in detail – how the mind or consciousness can be redefined and widened by technology. This cleverly opens the door for the slippery slope arguments as they rough up mass media and, inevitably ‘surveillance capitalism’ is invoked, before beating up Facebook, the IoT and the quantified self.

They see themselves as policing determinism and fighting harmful influence through two principles:              

1. Freedom to be off
2. Freedom from engineered determinism

Nozick’s ‘experience machine’ is constantly hauled in and although a fine thought experiment, I doubt that it has more than instrumental use to bolster the dystopian future they fear. Their suggested new framework cleverly proposes a reverse Turing test, where we test to see if we humans are becoming machines. This drifts off into a discussion of free will and engineered determinism but I fear that good philosophy has been sacrificed on the altar of their slippery slope hypothesis.

It does have some innovative ideas, such as a BBC style social network and more mainstream ideas like net neutrality and legal reforms strengthening the rights of individuals and regulation. They end, for example, with GDPR’s principle of consent as a good example of how things should evolve. But all too often it slips down its own slippery slope towards giving good old capitalism, markets, Taylorism and Fordism a kicking. 

To be fair, their case is detailed and well argued. I found the book thought provoking and although it clearly has some truths, the slippery slope is, in reality, perhaps more of a dialectic between minds and machines. By presenting a rather one-sided analysis, largely ignoring the benefits of new technology, they weaken their objectivity. That said, it is a good exposition of what could be called a weak dystopian position, stopping short of the full dystopian visions of an existential AI apocalypse.

This book will also introduce you to some interesting thinkers such as Searle, Weizenbaum, Chalmers, Clark, Nozick and many others but Daniel Dennett seems like a bizarre omission. 

Let's give Technology an -ology...

We see technology as a noun, not a discipline or subject. There is no -ology for techn-ology, stuck as it is somewhere between science and engineering. Yet this is an area of human endeavour that has shaped history, economics, sociology, psychology and philosophy.

The tendency is to see technology in mechanical, material terms, to be stuck in the old paradigm, much as in this vision of robot cleaners in 1899, when the artist tried to imagine the year 2000. What we actually got was an AI driven Roomba. We also see this in the many books about technology, such as Usler's The History of Mechanical Invention and Brian Arthur's The Nature of Technology, although the latter is far more sophisticated in seeing combinations of technology as the deep driver. The word technology comes from the Green Tekhne (art, craft) and logia (writings). We still see technology as ‘tech’ not ‘ology’.

Economics

In economics, from Adam Smith’s The Wealth of Nations and his earlier The Theory of Moral Sentiments, the role technology in economic development became obvious. For Smith, the division of labour accelerates technological innovation as processes and procedures are automated, resulting in lower levels of employment and higher profits. He warned us of the dangers, as “People of the same trade seldom meet together, even for merriment and diversion but the conversation ends in a conspiracy against the public, or in some contrivance to raise prices.” This is almost oracle-like and can be applied directly to tax evading and rapacious tech companies. What Smith uncovered was the simple fact that technological progress is inevitable but what we do with it is politically optional.

Jump a century and Marx gives us a deeper critique but still sees technology as an object that diminishes labour and allows the exploitation of production by capitalists. In a fascinating Fragment on Machines (from his notebooks, the Grundrisse) he prophesises a knowledge economy, where social knowledge becomes a commodity. This transcends classical Marxism and predicts what actually happened with the internet and now AI. His exact words on the effects of technology were “general social knowledge has become a force of production… under the control of the general intellect”. This idea is elaborated in Paul Mason’s Postcapitalism and has been described as ‘Marx beyond Marx’, a third form of capitalism, described by Antonio Negri’s followers as “cognitive capitalism”. What constitutes ‘value’ in this new economy has changed. It is no longer physical but psychological transactions, attention, eyeballs, knowledge, analysis, prediction, prescription, minds.

On the mechanics of technological change, a seminal text is Schumpter's Theories of Economic Development, bwhere cycles of economic development are seen as being driven by innovative technology as their cause.Carla Perez in Technological Revolutions and Financial Capital expands on the idea to identify specific cycles of over-effusive investment, slumps, then a period of fruitful investment that results in significant improvements in productivity. In other words, we overestimate technology in the short-term, underestimate it in the long-term.

Sociology

Beyond the mechanics of economics, we have had a deep analysis of the sociology of technology by, among many others, Marshall McLuhan and Neil Postman. McLuhan gave us the phrases ‘medium is the message’ and ‘global village’ which have so much resonance that they almost tip over into cliché. He was both an analyst of media and technology but also a visionary, predicted the web, invented the word ‘surfing’ for casual fragmentary media browsing and although he was dealing with the media a decade before the internet, his ideas, endure, through works like The Gutenburg Galaxy: The Making of Typographic Man and Understanding Media: The Extensions of Man. Postman in Amusing Ourselves to Death and Technolpoly, laid the ground work for subsequent analysis of the effects on technology on society. They saw the dialectic between technology and minds as having complex personal and social dimensions and consequences. Media and messages, tools shaping our minds, the dangers of amusing ourselves to death, with the advent of the web and AI, these issues have become even more complex, with even more profound consequences.

Psychology

At a deeper, and more detailed level, the psychology of technology has been studied  in works like The Media Equation by Nass and Reeves. The cognitive change from passive to active media is explored in Cognitive Surplus by Clay Shirky and a slew of works on the cognitive interaction between technology and the mind. It is important that we continue to read the literature from cognitive science on the role technology can play in improving teaching and learning. Without this bedrock of science we will be forever stuck in the world of fads and bogus and outdated ideas, like learning styles, Myers-Briggs and IAT tests on unconscious bias.

Philosophy

At an abstract level, the philosophy of technology was also been addressed by Descartes, Leibniz and Hobbes, more recently by Turing and Searle, then Sartre in Being and Nothingness and Heidegger in The Question Concerning Technology, where he questions the instrumental view of technology and searches for a deeper understanding of a relationship that has become much more problematic. This relationship between mind and machine was expanded in a seminal paper The Extended Mind, by Clark and Chalmers in 1997, with the idea of extending mind and cognition into the technosphere. Thomas Malone in Superminds, also sees in technology the formation of a network of immense power. This idea of a single network has been tempered by Niall Fergusson in The Square and the Tower, a reinterpretation of history around the idea of networks, where horizontal agoras or squares have been build but also vertical, hierarchical networks of power that attempt to control these structures. He thinks that we need a balance between these types of networks. Daniel Dennett has taken an even more expansive view, in his synthesis of the mind, natural world and technology, within the context of evolution, in From Bacteria too Back and Back. 

On the back of this interest in the economic, sociology, psychology and philosophy of technology, moral philosophy (ethics) has come to the fore. Dennett sees technology as being ‘competent without comprehension’ and is more sanguine about the dangers than some others. Re-engineering Humanity by Frischmann and Selinger is one such text, a detailed analysis of the slippery-slope of technological creep that may undermine society without us even being aware of its influence. Stuart Russell, in Human Compatible, also sees the problem as one of control.

The reason I have attempted to uncover these lines of literature, is that those of us working in the field, I feel, need sometimes to take to the higher ground. Far too much debate takes place at the level of us versus them, ignoring the complexity and subtleties of the field. Too often we get simplistic futurism or contrarianism. I have only touched upon the rich seams of literature in each of these strands. If we weave them together we get a strong rope by which we can pull the subject up into a more respectable level and see techn-ology as an -ology in itself. 

This piece was inspired by Nigel Paine, who interviewed me on this very topic last week for Learning TV. Thanks Nigel.

Ethics, Experts and AI

Ethics of AI has been discussed for decades. Even before Generative AI hit the world, the level of debate, from the philosophical to regulatory - was intense and at a high level. It has got even more intense over the last few months.

It is an area that demands high end thinking in complex philosophical, technical and regulatory domains. In short, it needs expertise. In public domain, the spate of Lex Friedman podcasts have been excellent - the recent Altman and Yudkoswsky ChatGPT programmes, in particular, represent the two ends of the spectrum. I also recommend high-end AI experts, such as Marcus, Brown, Hassabis, Karpathy, Lenat, Zaremba, Brockman and Tegman. At the philosophical level, Chalmers, Floridi, Bostrom, Russell, Larson, and no end of real experts have published good work in this field. They are now always easy to read but are worth the effort if you want to enter the debate. As you can imagine there is a wide spectrum from the super-optimists to catastrophic pessimists and everything in between. However, AI experts tend to be very smart and very academic, therefore often capable of justifying and taking very certain moral positions, even extremes, overdosing on their own certainty. 

Debate in the culture

In popular culture dozens of movies from Metropolis onwards have tacked the issues of mind and machine, more recently the excellent Her and ex machina, along many series, such as Black Mirror have covered many of the moral dilemmas. Harari covers an issue in Homo Deus and here's no end of books, some academic, some more readable and some pot-boilers. No one can say this topic has received no attention.

Catastrophists

At one end there are the catastrophists like Yudkoswsky, Leahy and Musk clustered around the famous Open Letter. The people behind the letter, the Future of Life Institute, have an ax to grind and couldn't even get the letter right, as it had fake names and some have backtracked. There is also something odd about a self-selecting group claiming that we are all stupid and easily manipulated, whereas they are enlightened and will fix it, then release to to use on their say so.

Pessimists

It is hard to align the catastrophists with the pessimists, like Noam Chomsky, who basically says, 'nothing to see here, move on'. There have been harbingers, not of doom but disinterest in LLMs, as they thought they would produce little that was useful. We can, I think, safely say they were wrong. They may well be right on such models reaching limits of functionality but in tandem with other tools they are here to stay.

Pragmatists

Then there’s a long tail of pragmatists, with varying levels of concern, from Larson, Hinton (his vision of an alternative Mortal Computer is worth listening to), as is Sam Altman and Yann LeCun, who thinks the open letter is a regressive, knee-jerk reaction, akin to previous censorious attitudes in history towards technology. Everyone of any worth or background in the field rightly sees problems, that is true of ant technology, which is always a double -edged sword. Unlike the catastrophists I have yet to read an unconditional optimists, who sees absolutely no problems here. In education, for example.some big hitters, such as Bill Gates and Salman Khan have put their shoulders into the task of realising the he benefits this technology has in learning.

Regulatory bodies

Further good news is that the regulatory bodies publish both their thinking and results and they have been, on the whole, deep, reasoned and careful. Rushed legislation that is too rigid does not respond well to new advances and can cripple innovation and I think they have been progressing well. I have written about this separately but the US, UK, EU and China, all in their different ways have something to offer and international alignment seems to be emerging.

My pragmatist view

I am aligned with LeCun and Altman on this and sit in the middle.The technology changes quickly and as it changes I take a dynamic view of these problems, a Bayesian view if you will, revising my view in the light of new approaches, models, data and problems as they arise. This was after a lot of reading, listening and the completion of my books on ‘AI for Learning’ where I looked at a suite of ethical issues, then ‘Learning Technologies’ where I wrote in depth about the features of new technologies such as writing, printing, computers, the internet and AI, including their cultural impact and the tendency for counter-reformations. One can see how all of these were met with opposition, even calls to be banned, certainly censored. I have lived through moral panics on calculators, computers, the internet, Wikipedia, social media, computer games and smartphones. There is always a ‘ban it’ movement.

Alignment

I’d rather take my time, as scalable alignment is the real issue. It is not as if there is one single, universal set of social norms and values to align to. Everybody's in an echo chamber says the people who think they're not. Alignment is therefore tricky, and needs software fixes (guardrails), human training of models and moderation. It may even need adjustments for different cultural contexts.

This is a control issue and we control the design and delivery of these systems, their high-level functions, representations and data. The only thing we don’t control is the optimization. That’s why there is no chance, for now, that these systems will destroy our civilization. As Lecun said “Some folks say "I'm scared of AGI. Are they scared of flying? No! Not because airplanes can't crash. But because engineers have made airliners very safe. Why would AI be any different? Why should AI engineers be more scared of AI than aircraft engineers were scared of flying?" The point is that we have successfully regulated, worldwide, cars, banking, pharma, encryption, the internet. We should look at this sector by sector. The one that does worry me is the military.

Escape

The big argument is that, without stopping it now, AGIs could learn to pursue goals which are undesirable (i.e. misaligned) from our human goals. They may, for example,  literally escape out onto the internet and autonomously cause chaos and real harm. I have seen no evidence that this is true, although the arguments are complex and I could be convinced. The emphasis in Generative AI into switch from pure moderation, the game of swatting issues as they arise, to training through RLHF (Reinforcement Learning through Human Feedback). This makes sense.

Ghosts in the machine

I’m wary of people soaked in a sci-fi culture, or more recently a censorious culture, coming up with non-falsifiable dystopian arguments, suggestions and visions that have no basis in reality. It is easy to project ideas into the depths of a layered neural network but much of the that projection is of ghosts in the machine. I’m also wary of those who are activists, not practitioners with an anti-tech agenda and who don’t really do ethics, in the sense of weighing up the benefits and downsides, but want to focus entirely on the downsides. I’m also wary of people bandying around general hypotheses on misinformation, alignment and bias, without much in the way of empirical data or definitions.

In fact, the signs so far on ChatGPT4 are that is has the potential to do great good. Sure you could, like Italy ban it, and allow the EU, despite its enormous spend on research, to fall further behind as investors freeze all investment across the EU. I’m with Bill Gates when he says the benefits could be global, with learning the No 1 application. In a world where there are often poorly trained teachers with classes of up to 50 children and places where there is one Doctor for every 10,000 people, the applications are obvious. This negativity over generative AI could do a lot of harm and have bad economic consequences. So let's not stop it for six months and not look at those opportunities.

Bibliography

The Oxford handbook of ethics of AI by Dubber, M. D., Pasquale, F., & Das, S. 

Ethics of artificial intelligence by Matthew S. Liao

AI Narratives: A history of imaginative thinking about intelligent machines by Stephen Cave, Kanta Dihal, & Sarah Dillon 

Human Compatible by Stuart Russell

Re-Engineering Humanity by Brett Frischmann and Evan Selinger

Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence by Patrick Lin, Keith Abney, and George A. Bekey

Artificial Intelligence Safety and Security edited by Roman V. Yampolskiy

Robot Rights by David J. Gunkel

Artificial Intelligence and Ethics: A Critical Edition" by Heinz D. Kurz and Mark J. Hauser

Moral Machines: Teaching Robots Right from Wrong by Wendell Wallach and Colin Allen

Artificial Intelligence and the Future of Power: 5 Battlegrounds" by Rajiv Malhotra

The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power by Shoshana Zuboff

AI Ethics by Mark Coeckelbergh