Review
Culture
5 min read

For love there is no charge

Out of mind old people are at the centre of Allelujah! Sian Brookes reviews the film adaptation of Alan Bennett’s play.

Sian Brookes is studying for a Doctorate at Aberdeen University. Her research focuses on developing a theological understanding of old age. She studied English and Theology at Cambridge University.

In a hall decorated for a celebration a person stands in front of a seated group, all have their arms raised in celebration.
Jazz hands at the hospital.
BBC Films.

Spoiler alert – this film review reveals significant elements of the plot. 

Allelujah! is not a film that shies away from the big issues. In fact, you would be hard pressed to find a big issue this comedy/political commentary/drama/part-thriller doesn’t at least make reference to (and yes, it spreads itself across all of these genres too). With such an eclectic approach it is difficult at times to keep up with the narrative, and the deeper meaning of the film. Based on the Alan Bennett play, the plot centres around The Bethlehem, a small northern hospital for geriatric patients, which is facing closure due to the Tory government’s efficiency drive. It focuses on two members of staff, Alma Gilpin, a stoic and matter-of-fact but seemingly excellent nurse who has served the hospital her entire career, and a younger Dr Valentine. Other protagonists include an ex-miner patient and his son, a management consultant who has “made it” to London and is currently advising the Health Secretary to close hospitals such as the one in question for the sake of government finances. 

Whether it’s politics or the personal, this film has it all. It deals with levelling up, the cultural and economic gap between the north and south, the challenges of budget cuts in the NHS, the problems of a national health service claiming to 'care' but with managers more preoccupied by Westminster’s economic priorities. It depicts families waiting for older relatives to die in order to grab their inheritance, the broken relationship between an ageing man and his son, and those all-important stories of the older patients’ lives well-lived. And yet as the story line develops, a plot twist emerges which comes to overshadow the entire film, and in the process speaks to what is perhaps the most poignant of the many discussions it raises. Nurse Gilpin, who, until now has appeared consistently caring and committed to her patients, has been quietly administering fatal beakers of milk and morphine to those who she deems to be on “her list” of those who most need relief from their situation. When confronted by the doctor she justifies her actions with a multifaceted answer based on the requirement to provide more beds to a broken healthcare system, but also insisting “I had ended someone’s suffering”.  

When Dr Valentine remarks, “I like old people” a visitor responds “not even old people like old people”.

The manner in which Nurse Gilpin goes about what is effectively enforced euthanasia, is deeply chilling. And yet her reasoning is not entirely foreign to us – to end suffering could be deemed a noble cause. In fact, the need to simply delete the reality of suffering, particularly the suffering of the old is one that perhaps is not so uncommon. Throughout Allelujah!,we are reminded of our tendency to run from, to detest, to reject the suffering of the elderly in our society. When Dr Valentine remarks, “I like old people” a visitor responds “not even old people like old people”. A teenage intern declares to a patient “I hope I never live to be your age”. At the same time, characters look back on the days “when the elderly weren’t farmed out”, and questions are asked of families “if they love them, why do they put them away?”. A very good question. Of course, care needs are often too great for families to endure, yet it is still important to ask why the suffering of the old has become a professionalised service, which most of us avoid at all costs. Perhaps the answer to this is that we don’t like to watch the old suffer, we don’t like to watch them die, because their suffering and their death remind us of our future selves, our future suffering, our future death. In our sanitised, anything-is-possible-with-medicine-and-science society, death and the suffering that comes with it, is something from which we flee at all costs. Instead of acknowledging and working with it, we would rather pretend it wasn’t there at all.  

And yet, even as we try to avoid it, suffering and death are both certain parts of all our futures. 100% of us will die. For Nurse Gilpin, the solution to this is to bring on death prematurely, to erase the pain, overcome the misery by offering a false hope – that it doesn’t need to exist at all. In direct contrast to this, in a film which is littered with Christian references (Allelujah, The Bethlehem), there is a different approach taken by a messiah-type figure who seems to get everything right. Dr Valentine is compassionate and understanding. He not only challenges the political systems which undermine those most at the margins of society, but also has the kind of bedside manner we would all hope for in a doctor. In a closing monologue Dr Valentine utters the words of the doctors in the NHS, “We will be here when you are old, and we would die for you, we are love itself and for love there is no charge”.  

It is this suffering with which is so compelling, this suffering with which is truly sacrificial.

Nurse Gilpin and Dr Valentine offer two fundamentally different approaches to end of life care. One hastens the end quickly, deletes the suffering as efficiently as possible in order to make way for those in less pain. The other sits with those who suffer, holds their hand, gently cares for the human person that is in front of them. Even more, and perhaps most significantly Dr Valentine does not only watch from afar, but is willing to suffer himself for the sake of those in pain - working tirelessly, giving himself over day after day, fighting on with little sleep for limited pay just to make things a little less painful. It is this suffering with which is so compelling, this suffering with which is truly sacrificial, this suffering with which speaks of something much greater than politics, efficiency or inheritance, this suffering with which is indeed “love itself”, completely free of charge.  This is the logic that Christians see in the ancient notion of the incarnation, celebrated every Christmas, of God with us. This is what our older people need, this is what we will all need when we grow old. Let us only hope that when we get there, we find the one who is willing to offer it.

Article
AI
Culture
10 min read

We’ll learn to live with AI: here’s how

AI might just help us with life’s dilemmas, if we are responsible.

Andrew is Emeritus Professor of Nanomaterials at the University of Oxford. 

Two construction workers stand and talk with a humanoid AI colleague.
Nick Jones/Midjourney.ai

Anxiety about algorithms is nothing new.  Back in 2020, It was a bad summer for the public image of algorithms. ‘I am afraid your grades were almost derailed by a mutant algorithm’, the then Prime Minister told pupils at a school. No topic in higher education is more sensitive than who gets a place at which university, and the thought that unfair decisions might be based on an errant algorithm caused understandable consternation. That algorithms have been used for many decades with widespread acceptance for coping with examination issues ranging from individual ill health to study of the wrong set text by a whole school seems quietly to have slipped under the radar.  

Algorithmic decision-making is not new. Go back thousands of years to Hebrew Deuteronomic law: if a man had sex with a woman who was engaged to be married to another man, then this was unconditionally a capital offence for the man. But for the woman it depended on the circumstances. If it occurred in a city, then she would be regarded as culpable, on the grounds that she should have screamed for help. But if it occurred in the open country, then she was presumed innocent, since however loudly she might have cried out there would have been no one to hear her. This is a kind of algorithmic justice: IF in city THEN woman guilty ELSE woman not guilty.  

Artificial intelligence is undergoing a transition from classification to decision-making. Broad artificial intelligence, or artificial general intelligence (AGI), in which the machines set their own goals, is the subject of gripping movies and philosophical analysis. Experts disagree about whether or when AGI will be achieved. Narrow artificial intelligence (AI) is with us now, in the form of machine learning. Where previously computers were programmed to perform a task, now they are programmed to learn to perform a task.  

We use machine learning in my laboratory in Oxford. We undertake research on solid state devices for quantum technologies such as quantum computing. We cool a device to 1/50 of a degree above absolute zero, which is colder than anywhere in the universe that we know of outside a laboratory, and put one electron into each region, which may be only 1/1000 the diameter of a hair on your head. We then have to tune up the very delicate quantum states. Even for an experienced researcher this can take several hours. Our ‘machine’ has learned how to tune our quantum devices in less than 10 minutes.  

Students in the laboratory are now very reluctant to tune devices by hand. It is as if all your life you have been washing your shirts in the bathtub with a bar of soap. It may be tedious, but it is the only way to get your shirts clean, and you do it as cheerfully as you can … until one day you acquire a washing machine, so that all you have to do is put in the shirts and some detergent, shut the door and press the switch. You come back two hours later, and your shirts are clean. You never want to go back to washing them in the bathtub with a bar of soap. And no one wants to go back to doing experiments without the machine. In my laboratory the machine decides what the next measurement will be.  

Suppose that a machine came to know my preferences better than I can articulate them myself. The best professionals can already do this in their areas of expertise, and good friends sometimes seem to know us better than we know ourselves. 

Many tasks previously reserved for humans are now done by machine learning. Passport control at international airports uses machine learning for passport recognition. An experienced immigration officer who examines one passport per minute might have seen four million faces by the end of their career. The machines were trained on fifty million faces before they were put into service. No wonder they do well.  

Extraordinary benefits are being seen in health care. There is now a growing number of diagnostic studies in which the machines outperform humans, for example, in screening ultrasound scans or radiographs. Which would you rather be diagnosed by? An established human radiologist, or a machine with demonstrated superior performance? To put it another way, would you want to be diagnosed by a machine that knew less than your doctor? Answer: ‘No!’ Well then, would you want to be diagnosed by a doctor who knew less than the machine? That’s more difficult. Perhaps the question needs to be changed. Would you prefer to be treated by a doctor without machine learning or by a doctor making wise use of machine learning?  

If we want humans to be involved in decisions involving our health, how much more in decisions involving our liberty. But are humans completely reliable and consistent? A peer-reviewed study suggested that the probability of a favourable parole decision depended on whether the judges had had their lunch. The very fact that appeals are sometimes successful provides empirical evidence that law, like any other human endeavour, involves uncertainty and fallibility. When it became apparent that in the UK there was inconsistency in sentencing for similar offences, in what the press called a postcode lottery, the Sentencing Council for England and Wales was established to promote greater transparency and consistency in sentencing. The code sets out factors which judges must consider in passing sentence, and ranges of tariffs for different kinds of crimes. If you like, it is another step in algorithmic sentencing. Would you want a machine that is less consistent than a judge to pass sentence? See the sequence of questions above about a doctor.  

We may consider that judicial sentencing has a special case for human involvement because it involves restricting an individual’s freedom. What about democracy? How should citizens decide how to vote when given the opportunity?  Voter A may prioritise public services, and she may seek to identify the party (if the choices are between well identified parties) which will best promote education, health, law and order, and other services which she values. She may also have a concern for the poor and favour redistributive taxation. Voter B may have different priorities and seek simply to vote for the party which in his judgement will leave him best off. Other factors may come into play, such as the perceived trustworthiness of an individual candidate, or their ability to evoke empathy from fellow citizens.  

This kind of dilemma is something machines can help with, because they are good at multi-objective optimisation. A semiconductor industry might want chips that are as small as possible, and as fast as possible, and consume as little power as possible, and are as reliable as possible, and as cheap to manufacture as possible, but these requirements are in tension with one another. Techniques are becoming available to enable machines to make optimal decisions in such situations, and they may be better at them than humans. Suppose that a machine came to know my preferences better than I can articulate them myself. The best professionals can already do this in their areas of expertise, and good friends sometimes seem to know us better than we know ourselves. Suppose also that the machine was better than me at analysing which candidate if elected would be more likely to deliver the optimal combination of my preferences. Might there be something to be said for benefitting from that guidance?  

If we get it right, the technologies of the machine learning age will provide new opportunities for Homo fidelis to promote human flourishing at its best.

By this point you may be sucking air through your intellectual teeth. You may be increasingly alarmed about machines taking decisions that should be reserved for humans. What are the sources of such unease? One may be that, at least in deep neural networks, the decisions that machines make may be only as good as the data on which they have been trained. If a machine has learned from data in which black people have an above average rate of recidivism, then black people may be disadvantaged in parole decisions taken by the machine. But this is not an area in which humans are perfect; that is why we have hidden bias training. In the era of Black Lives Matter we scarcely need reminding that humans are not immune to prejudice.  

Another source of unease may be the use to which machine learning is put for commercial and political ends. If you think that machine learning is not already being applied to you, you are probably mistaken. Almost every time you do an online search or use social media, the big data companies are harvesting your data exhaust for their own ends. Even if your phone calls and emails are secure, they still generate metadata. European legislation is better than most, and the Online Safety Act 2023 will make the use of Internet services safer for individuals in the United Kingdom. But there is a limit to what regulation can protect, and 2024 is likely to see machine learning powerfully deployed to sway voters in elections in half the world. Targeted persuasion predates AI, as Othello’s Iago knew, but machine learning has brought it to an unprecedented level of industrialisation, with some of the best minds in the world paid some of the highest salaries in the world to maximise the user’s screen time and the personalisation of commercial and political influence.  

Need it be so? In some ways advances in machine learning are acting as the canary in the mine, alerting us to fundamental questions about what humans are for, and what it means to be human. The old model of Homo economicus—rational, selfish, greedy, lazy man—has passed its sell-by date. It is being replaced by what I like to call Homo fidelis—ethical, caring, generous, energetic woman and man. For as long as AGI remains science fiction, it is up to humans to determine what values the machines are to implement. If we get it right, the technologies of the machine learning age will provide new opportunities for Homo fidelis to promote human flourishing at its best.  

Whatever the future capabilities of machines, they cannot be morally load-bearing because humans are self-aware and mortal, whereas machines are not.

Paul Collier and John Kay

Christians have been thinking about what it means to be human for two millennia, building on what came before, and so they ought to have something to contribute to how humans flourish. In It Keeps Me Seeking, my co-authors and I ask our readers to imagine that they were writing about three thousand years ago for people who knew nothing of modern genetics or psychological science about what it means to be human. ‘You are writing for a storytelling culture, and so you would probably put it in the form of a story. Let’s say you set it in a garden. The garden is pleasant, but it is also designed for character formation, and so there is work to do, and also the possibility for a hard moral choice. You want to convey that humans need social interactions (for the same reason that solitary confinement is a severe punishment), and so you try the literary thought experiment of having one solitary man and letting him encounter animals and name them. Animals can be useful and they can be good company. But ultimately no animals, not even a dog, are fully satisfactory as partners in work and companions in life. Humans need humans. An enriching component of human relationships is sex. So, the supreme gift to the solitary man in our story is companionship with an equal who is both like and unlike; a woman. It is hardly a complete account, but it is a good start. Oh, and there is one other aspect. They should be free of the shame which lies at the root of so much psychological disorder.’  

As far as it goes, would you regard such an account as complete? If not, what would you add next? You can see where this is going. To be human you need to be responsible. So, you let the humans face the moral choice. You can even include an element of disinformation to make the choice harder. And then when it goes horribly wrong you let them discover that they are responsible for their actions, and that blaming one another does not help. If you have God in your story, then (uniquely for the humans) responsibility consists of accountability to God. This is how human distinctiveness was addressed in early Jewish thought. As an early articulation that to be human means to be responsible, the story of Adam and Eve is unsurpassed.  

In Greed is Dead, Paul Collier and John Kay reference Citizenship in a Networked Age as brilliantly elucidating the issue of morally pertinent decision-taking. They write, ‘Whatever the future capabilities of machines, they cannot be morally load-bearing because humans are self-aware and mortal, whereas machines are not. Machines can be used not only to complement and enhance human decision-making, but for bad: search optimisation has already morphed into influence-optimisation. We must keep morally pertinent decision-taking firmly in the domain of humanity.’  

The nature of humanity includes responsibility—for wise use of machine learning and much more besides. Accountability is part of life for people with widely differing philosophical, ethical, and religious world views. If we are willing to concede that accountability follows responsibility, then we should next ask, ‘Accountable to whom?’