The Ethics of AI in Education

A presentation given at the Middle East Teaching and Learning Conference on 26th August 2020.

The video is available here:

The slides are available here:

 

This presentation looks at how AI works, how it is being used presently in Education and then outline some concerns about how AI might be used in education in the future. 

In it I argue that AI has a much greater part to play in Education – particularly in making education more widely available in the developing world and in reducing the cost of education. The talk then moves on to discuss general ethical concerns about how AI is being used in society, looking at the issue of how we program autonomous vehicles as a case in point. I then outline five areas of concern about the use (and potential abuse) of AI in education arguing that we need to have a much more informed debate before things go too far. With this in mind, I close with some suggestions for courses and reading that might help colleagues to become better informed about the subject.

Machines Like Me by Ian McEwan - Book Review/Summary

Ian McEwan's Machines Like Me is a novel which weaves the discussion of  a number of important philosophical questions about what it means to be human into an engaging inter-personal narrative.

The genius of the novel is that, unlike most futuristic novels, it is placed in the past. McEwan sets his novel in Thatcher's Britain of 1992 at the time of the Falklands conflict and its immediate aftermath. But there is a twist - this is a parallel history of the C20 and a charismatic and popular Tony Benn (rather than Michael Foot), is leader of the opposition. (Indeed there are feint echoes at times of popular reactions to Theresa May and Jeremy Corbyn in 2018).

A particularly fine touch is that McEwan explores the 'What if Alan Turing had not eaten the apple, and, instead, had lived into old age as the pioneer of computer science?' In this history of the C20 a number of technological advances of our recent past (Computers beating human champions at Chess and Go) and near future (autonomous cars, stem cell therapies) are transposed back to the 1950s, 1960s and 1970s. 

At the outset, the author places a high-functioning self-conscious humanoid robot, Adam, into a banal domestic setting with Charlie, the narrator, and Miranda his girl-friend. Herein is the vehicle to raise a number of key philosophical questions about the relationship of humans to intelligent machines.

• Can robots/AI have feelings? 
• fall in love? (p.118)
• feel existential pain/angst? (p.181, p.234)
• commit suicide? (p.175)
• Is it murder to kill a self-conscious robot? (p.303)

On the way, McEwan discusses a range of important spin-off issues, such as the possibility the ultimate integration of man and machine in augmented humanity through a brain-machine interface (p.148) and Universal Basic Income funded by a tax on robots (p.169).

This novel is a useful contribution to the debates that we should be having about AI/Robots before they land on our doorstep. 

Horses pulling Automobiles: The Impact of AI on Education

Cars were forbidden in the town of Nantucket, Massachusetts, until 1918 – some 25 years after the Duryea brothers set up the first car manufacturing company in America. The story goes that, during this time, Clinton Folger, the Island’s postman, towed his "gasoline buggy" to the state highway so that he could then drive to Siasconset on the other side of Nantucket Island to deliver the mail. The picture of the horse-drawn automobile is an apt metaphor for what is happening with Artificial Intelligence in Education today. 

Artificial Intelligence (AI) learning platforms began to be launched at the primary and secondary education market a little over a year ago. We are still at the pioneering stage where the early adopters are trialing products, suggesting improvements and trying to evaluate their impact on teaching and learning. 

It would be wrong to see AI learning platforms simply as the next in a long-line of new technologies which have been harnessed to enhance what we are doing in the classroom, although there is little doubt that they can do this. AI is likely to transform nearly every industry; indeed, it will change society as a whole. It goes without saying that it has the potential to disrupt education. To understand the drivers behind this disruptive potential of AI we need to consider Education from a global perspective. 

Education is very big business. According to IBIS Capital [Global EdTech Industry Report 2016], education was $5 trillion industry globally in 2014 and is growing at $600 billion a year. However, only 2% of it is digitised – education as an industry is a ‘late adopter’ at best. So, EdTech is increasingly being seen as a ripe market for investors. Three factors make it very attractive: the importance and cost of education to Governments and parents; the global teacher shortage and the unsustainability of the present model of one teacher to 20-30 students; and the abundant scope for new markets (there are currently 263m children in the world not in education). Thus, the Holy Grail of EdTech is an effective AI platform that will solve these problems in a scalable and affordable way by providing a personalised learning experience with minimal teacher input. There is scope here to be the ‘Amazon of Education’ - no wonder the Venture Capitalists are turning their attention to EdTech.

To date, the AI platforms which have been launched are automating and enhancing aspects of the teaching and learning process. Typically, they conduct a base-line test, they introduce relevant content, which they then test, and using adaptive algorithms provide personalised feedback to the learner, their parents and the teacher. This is helpful but it not really embracing the full transformatory power of AI. In short, we are using AI in education to achieve C20 educational outcomes. It is rather like the situation faced by the postman of Nantucket: we have invented the automobile, but we are being forced by the regulators to pull it along with a horse because it doesn’t fit with their outdated view of the world. There is huge latent potential in the way in we AI is being developed that will change radically the educational landscape globally and in the UK. 

Teacher-Pupil contact time is the most precious resource that schools have. This is particularly true of specialist teacher time (e.g. suitably qualified Physics teachers) which is already a scarce resource. Looking ahead five to ten years, AI learning platforms will not replace teachers, but they will change what teachers do, especially in secondary schools as new technologies, including AI, allow schools to make the most of the limited teacher time available. Looking at things globally, in this Brave New World, the quality of the education available will be driven by cost; and the amount and nature of the human contact time available will determine the price-point. 

As Clayton Christensen has argued, disruptive technologies get their foothold in new and emerging markets and then gradually work their way into the mainstream. On this basis it is possible to predict how AI will transform education. AI learning platforms will have their greatest impact in markets where there currently there is no education available. Budget Secondary Education will not have face-to-face contact with qualified teachers but will be delivered totally through online courses on learning platforms. This is not a form of schooling that is recognisable to western educationalists, but for many young people around the world this will be better than the present situation of receiving no education at all. Moving up to Mid-Range Secondary Education, this will be delivered through blended learning programmes which combine AI learning platforms, subject specialist teaching via Virtual-Reality conferencing, and some face-to-face contact with teachers in a bricks-and-mortar environment. The US Public School system is in the vanguard of this (for an overview see Keeping Pace with K-12 Digital Learning Reports). However, to date, there has been little appetite for adopting this model in the UK as was witnessed when counsellors rejected the plans to use Blended Learning at the Ark Pioneer Academy in Barnet (see Tes 30/01/2017). Finally, it will only be in top fee-paying private schools and in the state sector in the wealthiest countries of the world that Premium Secondary Education that will be delivered by specialist teachers in classrooms. Face-to-face teaching in a class of 20-30 will be a luxury (indeed from a global perspective, it already is). Here AI platforms will enhance and augment the learning process. 

Given the level of investment by the private sector into EdTech, it is clear that AI in Education is here to stay and the days of the Horse-drawn Automobile are coming to an end. So how best to prepare for the future? Perhaps the most important thing that educationalists can do at this time is inform themselves as to how AI works and to join the debate about what constitutes the ethical use of AI in education before it’s too late.

This article was published in Tes as 'Teaching's use of AI is like a horse-drawn automobile' 16/05/2019

The Machines are coming: Automated Systems and AI in Education

A Presentation given at the COBIS Annual Conference in London on Monday 13th May 2019



The presentation looks at:

1. How automated systems can handle data; 
2. How automated systems can provide live data to parents bringing an end to school reports 
3. AI in Education 
4. Will AI ever replace teachers?

Solomon’s Code by Olaf Groth and Mark Nitzberg – Book Summary

This is a book about Artificial Intelligence that deliberately poses more questions than it answers. Groth and Nitzberg’s aim is to outline some of the most important multi-disciplinary debates that need to take place if AI ultimately is going to be beneficial to humanity. 

The authors take a fundamentally optimistic (but not utopian) view of AI and how it can benefit society, but this is grounded in the real politik of twenty-first century multi- and inter-national relations. This optimism is seen in the espousal of a model of human-AI symbiosis (Chapter 3) which enhances humanity: 

a “symbiotic relationship between artificial, human and other types of natural intelligence can unlock incredible ways to enhance the capacity of humanity and environment around us.” (p.69) 

This is worked out in a number of ways through discussion of a number of important social debates: justice and fairness, privacy, security, surveillance and changing patterns of work. In the first half of the book, Groth and Nitzberg discuss a range of important philosophical questions thrown up by AI about the nature of what it is to be human: self-consciousness (pp.96ff), human personhood, autonomy and free will; reshaping the sense of the self (p.76) and the ability for humans to change their values and beliefs over time (p.88). 

The second half of the book is a call to arms to put in place a regulatory framework (“guardrails”) for the use of AI which maximises the benefits of AI, whilst mitigating potential harm. They argue that this will include drafting a “Digital Magna Carta” which defines human freedoms in the age of AI (p.232). In so doing, the authors recognise just how difficult this is likely to be. Indeed, about a third of the book is devoted to outlining the complexities of the emerging geopolitical context for these discussions. 

There is an excellent discussion of “the forces that shape the world’s divergent AI journeys” (Chapter 4), which outlines the different attitudes to AI and technology around the world: “the Digital Barons” (Google, Facebook, Amazon, Alibaba and Baidu); “the Cambrian Countries (US and China); “the Castle Countries” (Russian and Western Europe); “the Knights of the Cognitive Era” (military/defence-based AI – US, China and Israel); “the Improv Artists” (other countries developing aspects of AI – Nigeria, Indonesia, India and Barbados); “Astro Boy” (Japan); and “the CERN of AI” (Canada – the open-source concept of an international network of data generators). 

What comes through this discussion is the range of ways that power, trust and values are being played out across societies, often driven by different regional philosophical traditions. For example, the influence of Taoist, Confucian and Communist thought on China; and the social challenges of an ageing population in Japan mean that these countries have fundamentally different attitudes to the West on issues such as privacy and the relationship between humans to machines. The authors rightly point out that this philosophical diversity poses significant challenges for anyone seeking to formulate a universal approach to regulating the use of AI. 

In the authors’ analysis of ‘the race for global AI influence’ (Chapter 5) the battle for control of data and AI is tantamount to a new arms race which has the potential to reshape the political world order (Putin: the country that leads on AI “will become the ruler of the world” p.151) and discuss each of the main protagonists in turn: US, China, Russia and the EU. 

“Philosophies of regulation, influence and social and economic participation will conflict – as they should. Those clashes and their outcomes will coalesce around issues of values, trust and power” (p.163-4). 

The authors close (chapter 8) by discussing possible ways in which the community of nations might establish “a global governance institution with a mutually accepted verification and enforcement capacity” (p.233). In so doing they discuss the lessons learned from other recent multinational treaties and governance models, such as the Montreal Protocol to reduce chloroflurorocarbons, the Paris Agreement on climate change, the Organization of the Prohibition of Chemical Weapons (OPCW), and the UN Global Compact. In light of these, they argue instead for a “new governance” model which draws its legitimacy from “its inclusion and the robustness of the norms and standards it disseminates”, but which is aligned “with existing pillar of global governance such as the United Nations or the World Trade Organization” (p.249) 

The authors conclude that “the Machine can make us better humans” (p.253): 

“Combining the unique contributions of these sensing, feeling and thinking beings we call human with the sheer cognitive power of the artificially intelligent machine will create a symbio-intelligent partnership with the potential to lift us and the world to new heights.” (p.257) 

Surprisingly for a work of this quality and nature, the book has no index and only has limited referencing.

Artificial Intelligence, Ethics and Education

What is AI? 

Artificial Intelligence refers to those computer systems which are both autonomous and adaptive; i.e. they are systems which have the ability to perform complex tasks without constant guidance by a user and they also have the ability to improve performance by learning from experience. The process of getting computers to learn without being explicitly programmed is called Machine Learning. 

We are all familiar with the increasing role that AI is playing an increasing part in our lives. Machine-learning is managing our email junk folders; it is suggesting the next word when we are texting; it is labelling and organising our photo albums; and it makes suggestions on what we should buy next from Amazon or watch next on Netflix. Most of these functions rely on ‘Supervised’ Machine Learning algorithms that are developed on the basis of an initial training set of data, which is then supplemented as further information becomes available. 

AI and Ethics 

Ethical concerns about AI revolve around ‘algorithmic bias’ i.e. around the validity of the way in which the algorithm is constructed and usually around the nature of the training dataset. These arguments take three forms: 

1. Concerns about Bias: the training dataset on which the algorithm was originally constructed may not reflect the composition of the wider population. To take an extreme example, a dataset that is based on American billionaires is likely to be white, educated, aged over 45, male and, by definition, rich.
2. Concerns about Fairness: the training dataset is based on accurate historic data, but those data reflect unfair practices. For example, in 2011 the City of Boston MA launched ‘Street Bump App’ which maps the location of potholes that needed repairing around the city by collecting data from the accelerometer in the Smartphone. The app successfully collected data and saved the City time and money in surveying the roads. However, a review of the project after 12 months showed that a disproportionate number the potholes identified and repaired were in affluent middle-class areas, to the detriment of those in poorer areas. This was almost certainly because affluent middle-class residents were more likely to own a smartphone and were more likely to download the app. In similar vein, data scientist
   
   Cathy O’Neill, author of Weapons of Math Destruction, has voiced her concerns about the way in which the algorithms are being used in the US criminal justice system. The police are using historic arrest data as a proxy for crime data to drive preventative policing models. Because of this, the algorithm simply reinforces historic practice by sending the police back to the neighbourhoods which they are already over-policing; and are not sent to neighbourhoods which have crime, but those crimes are found. The irony is that, in these examples, the intention was to create algorithms which were free from human bias, however, because of the way in which they were constructed that had unintended consequence of perpetuating historic inequalities. 
3. Concerns about Unethical Behaviour: the dataset is deliberately skewed or designed to behave in a dishonourable way. AI is fundamentally an ethically neutral platform. It can be used or misused like any other technology. History teaches us that most technologies are misused at some point. 

In order to avoid historic or intentional bias, it is necessary to develop new protocols. Once designers deviate from historic data and endeavour to build an algorithm which is based on data which is deemed both unbiased and fair, they are presented with some quite serious ethical challenges. Here there will be parallels here to the debates about the value of positive discrimination in the workplace. One way to manage AI is to establish some protocols which will ensure that we avoid algorithmic bias - and here diversity is the key. There needs to be a Diversity of Background and a Diversity of Mindset of the team building the algorithms to avoid “group think”; a Diversity of Data that comprises any training set; and Diversity of Algorithmic Models used. 

Looking ahead it is likely that there will need to be formal regulation of algorithm design (rather akin the way in which financial services is regulated) which will entail the development of regulatory function of ethical audit. This role will ensure that algorithms are not subject to intentional or unintended bias. 

The Ethics of AI in Education 

The use of AI in education is in its infancy. We are beginning to see adaptive learning platforms, such as CenturyTech, being used in schools, primarily to supplement and support what teachers are doing in the classroom. Whether or not this is the first tentative step towards the ‘Holy Grail’ of fully adaptive and personalised learning that does not require teacher input is a debate for another day. Looking ahead it is like AI in Education is likely to pose some significant ethical issues. 

1. First, as those who are embroiled in GDPR know only too well, there are a whole range of concerns about the security, ownership and privacy of personal student data that is captured and stored within an AI platform. There will need to policies and protocols in this area. 
2. Secondly, there are concerns about the fairness of access to AI technologies and the potential for AI to increase the ‘Digital Divide’ between those who can afford access to the technology and those who don’t. 
3. Thirdly, there is a danger of having a biased training set on which educational AI technologies are founded reinforce social/ cultural/ etc. stereotypes. For example, it is quite possible that the dataset for an AI learning platform might be skewed because the early adopters all come from affluent fee-paying schools who can afford to provide access. 

Assessment Algorithms. 

Perhaps the greatest ethical issues might come around AI being used to make significant summative assessments of students’ abilities in the allocation of places at university or into the jobs market. We have already seen the ‘Big Four’ accountancy firms preferring their own assessment platforms to consideration of A-level and Degree results in order to find recruits who have the most potential (e.g. ‘Big Four’ look beyond academics – Financial Times 28/02/2016). It is quite possible to conceive of a time when both universities and employers, motivated from the noble intention of assessing potential and facilitating social mobility, will rely on their own assessment recruitment algorithms to identify suitable candidates. If this were to have it would be vital that any algorithm be subject to rigorous ethical audit to ensure that it meets a standard test of fairness. 

Final Remarks 

We have only begun to realise the potential that Artificial Intelligence has to shape C21 society and, sadly, social and ethical debate is struggling to keep up with the development of the technology. There needs to be an informed debate about the place of AI in society, and particularly of how it is going to be applied in education. In order to do this, we need a much greater understanding in society of how AI and Machine Learning work – and that is a challenge which I hope will be taken up by schools over the coming months and years.

This article was published in Digital Strategy Edition 2, March 2019 by the ISC Digital Strategy Group.

The Rise of AI

A keynote presentation given at the ISBA Digital Strategy and Cybersecurity Conference at the BMA in London on Wednesday 27th March 2019.
The presentation looks at the following areas:

1. What is AI?
2. The Ethics of Ai.
3. AI, Education and the #FutureSchool
4. The threat of AI to Fee-paying Education

Transforming Learning in a Millennial World

My keynote presentation at the BSME Annual Conference held at Yas Marina Conference Centre in Abu Dhabi on Wednesday 20th March 2019

The Ethics of AI

A theory of Knowledge Lecture given on Monday 4th March 2019 to the Lower Sixth at JESS, Dubai.

#FutureSchool - How AI, VR and Robots are transforming how children learn

Presentation given at the Middle East Schools Leadership Conference on Wednesday 10th October 2018

 

The Fourth Education Revolution – Anthony Seldon – Book Summary

The Fourth Education Revolution is about likely impact of Artificial Intelligence on society. Sir Anthony Seldon, the Vice-Chancellor of the University of Buckingham and previously Headteacher of two top HMC boarding schools, Brighton and Wellington Colleges, not only discusses the (AI) on education, but, perhaps more importantly, explores the philosophical and moral debates about the place of AI in society. Thus, this is an important book not just for those of us in education, but also beyond the profession. We could not be in better hands.

The book forms a logical structure argument for change. The first six chapters set the context for the debate by:

1. reviewing the first three educational revolutions; 
2. discussing what it means to be an educated person; 
3. discussing Five Intractable Problems with Conventional Education: 
4. discussing What is Intelligence? 
5. discussing What is Artificial Intelligence? 
6. reviewing the state of AI in the USA and the UK 

(Each of these chapters is worthy of consideration and is an interesting introduction and summary of these important areas)

The key chapter for secondary school educationists is his discussion of ‘The future of AI in Schools’ (Chapter 7). Here Seldon’s methodology (after Suskind and Suskind) is to establish a ten-part model for education by aggregating the tasks of the teacher and the student:

Five Traditional Tasks in Teaching: 

1. Preparation of material; 
2. Organisation of the classroom/ learning space; 
3. Ensuring that all students are engaged in learning; 
4. Setting and marking assignments; 
5. Preparation for terminal examinations and writing summative reports. 

Five Traditional Activities in Learning: 

1. Memorising knowledge; 
2. Applying the knowledge; 
3. Turning knowledge into understanding; 
4. Self-assessment and diagnosis; 
5. Reflection and the development of autonomous learning.

He then argues how each of five traditional factors in teaching will be transformed by AI over the coming decades: 

1. Preparation of material will be done by ‘Curation specialists . . whose job it is to work with AI machines to author and identify the most appropriate material for particular student profiles.’ p.189 
2. Organisation of the learning space: ‘Separate classrooms will disappear in time and replaced by pods and wide open, flexible spaces which can be configured for individual and flexible collective learning. Sensors will monitor individual students, measuring their physiological and psychological state, picking up on changes faster and more accurately than any teacher could.’ p.191 
3. Presentation of material to optimise learning/deeper understanding: ‘The flexibility of visual representation with AI allows material to be presented to students which renders much teacher exposition redundant.’ p.192 
4. Setting assignments and assessing/self-assessing progress: ‘Advances in real-time assessment enabled by AI will virtually eliminate this waiting period [the time lag between students being assessed and them receiving feedback on their performance} and ensure feedback comes when most useful for learning.’ pp.194-5. 
5. Preparation for terminal examinations and writing summative reports: ‘All this will be swept away by AI. . . . In its place will be attention to continuous data reporting, and real time feedback that will help students discover how to learn autonomously and how to address any deficiencies on their own.’ p.196 

And so to the $100.000.000 question: Will we need teachers in the future? Seldon is clear ‘We do not believe that it is either possible or desirable for AI to eliminate teachers from education’ but he goes on to point out that ‘the application of AI places more responsibility for learning in the hands of the student, for how their time is spent and on what, even from a young age.’ p.205. ‘AI will change however the job of the teacher forever. By supporting teaching in all their five traditional tasks, AI will usher in the biggest change the profession has ever seen.’ p.206. Interestingly Seldon recognises that remote teaching is a distinct possibility: ‘Imminent advances in virtual technologies will mean too that teachers no longer have to be physically present to offer their services.’ p.206

This is well drafted and highly informed argument. It was a joy to read. The only surprise and disappointment for me was that he did not address the 'Elephant-in-the-room' questions of how the traditional examination structures (especially GCSE and A-level in the UK) will be dismembered and on what time-scale. We can all see that the direction of travel is that GCSE and A-level will probably 'will be swept away by AI ' in twenty years' time, but how we get there and what the drivers will be is one of the greatest questions facing UK education over the coming years. These are essentially political questions and ones on which he, given his intimate knowledge of UK politics, Sir Anthony is uniquely qualified to comment - let's hope he does in due course.

How will AI transform the classroom of the future?

A presentation given that the Dubai Future Technology Week in Dubai on 2nd May 2018

 

Innovation and Digital Learning

A presentation given at the Education Experts Conference on Monday 5th March 2018 in Dubai.
This presentation considers four aspects of the #FutureSchool:

1. The Paperless Classroom; 
2. Blended Learning Programmes; 
3. AI, Pupil Tracking and the end of School Reports; and 
4. Robots in the Classroom to support Personalised Learning.

 

The Digital #FutureSchool - Automation, Innovation, Disruption

A Presentation given at the ISC Digital Strategy Group Conference held at Microsoft, Reading on 30th November 2017