Article
AI - Artificial Intelligence
Community
Culture
Education
5 min read

Artificial Intelligence needs these school lessons to avoid a Frankenstein fail

To learn and to learn to care are inseparable

Joel Pierce is the administrator of Christ's College, University of Aberdeen. He has recently published his first book.

A cyborg like figure opens the door to a classroom.
AI in the classroom.
Nick Jones/Midjourney.ai.

Recent worries expressed by Anthropic CEO, Dario Amodei, over the welfare of his chatbot bounced around my brain as I dropped my girls off for their first days at a new primary school last month. Maybe I felt an unconscious parallel. Maybe setting my daughters adrift in the swirling energy of a schoolyard containing ten times as many pupils as their previous one gave me a twinge of sympathy for a mogul launching his billion-dollar creation into the id-infused wilds of the internet. But perhaps it was more the feeling of disjuncture, the intuition that whatever information this bot would glean from trawling the web,it was fundamentally different from what my daughters would receive from that school, an education.  

We often struggle to remember what it is to be educated, mistaking what can be assessed in a written or oral exam for knowledge. However, as Hannah Arendt observed over a half century ago, education is not primarily about accumulating a grab bag of information and skills, but rather about being nurtured into a love for the world, to have one’s desire to learn about, appreciate, and care for that world cultivated by people whom one respects and admires. As I was reminded, watching the hundreds of pupils and parents waiting for the morning bell, that sort of education only happens in places, be it at school or in the home, where children themselves feel loved and valued.  

Our attachments are inextricably linked to learning. That’s why most of us can rattle off a list of our favourite teachers and describe moments when a subject took life as we suddenly saw it through their eyes. It’s why we can call to mind the gratitude we felt when a tutor coached us through a maths problem, lab project, or piano piece which we thought we would never master. Rather than being the pouring of facts into the empty bucket of our minds, our educations are each a unique story of connection, care, failure, and growth.  

I cannot add 8+5 without recalling my first-grade teacher, the impossibly ancient Mrs Coleman, gazing benevolently over her half-moon glasses, correcting me that it was 13, not 12. When I stride across the stage of my village pantomime this December, I know memories of a pint-sized me hamming it up in my third-grade teacher’s self-penned play will flit in and out of mind. I cannot write an essay without the voice of Professor Coburn, my exacting university metaphysics instructor, asking me if I am really saying what is truthful, or am resorting to fuzzy language to paper over my lack of understanding. I have been shaped by my teachers. I find myself repaying the debts accrued to them in the way I care for students now. To learn and to learn to care are inseparable. 

But what if they weren’t? AI seems to open the vista where intelligences can simply appear, trained not by humans, but by recursive algorithms, churning through billions of calculations on rows of servers located in isolated data centres. Yes, those calculations are mostly still done on human produced data, though the insatiable need for more has eaten through most everything freely available on the web and in whatever pirated databases of books and media these companies have been able to locate, but learning from human products is not the same as learning from human beings. The situation seems wholly original, wholly unimaginable. 

Except it was imagined in a book written over two hundred years ago which, as Guillermo del Toro’s recent attempt to capture that vision reminds us, remains incredibly relevant today. Filmmakers, and from trailers I suspect Del Toro is no different here, tend to treat the story of Frankenstein as one of glamorous transgression: Dr Frankenstein as Faust, heroically testing the limits of human knowledge and human decency. But Mary Shelley’s protagonist is an altogether more pathetic character, one who creates in an extended bout of obsessive experimentation and then spends the rest of the book running from any obligation to care for the creature he has made.  

It is the creature who is the true hero of the novel and he is a tragic one precisely because his intelligence, skills, and abilities are acquired outside the realm of human connection. When happenstance allows him to furtively observe lessons given within a loving, but impoverished family, he imagines himself into that circle of growing love and knowledge. It is when he is disabused of this notion, when the family discovers him and is disgusted, when he learns that he is doomed to know, but not be known, that he turns into a monster bent on revenge. As the Milton-quoting monster reminds Frankenstein, even Adam, though born fully grown, was nurtured by his maker. Since even this was denied creature, what choice does he have but to take the role of Satan and tear down the world that birthed him? 

Are our modern maestros of AI Dr Frankensteins? Not yet. For all the talk of sentient-like responses by LLMs, avoiding talking about distressing topics for example, the best explanation of such behaviour is that they simply are mimicking their training sets which are full of humans expressing discomfort about those same topics. However, if these companies are really as serious about developing a fully sentient AGI, about achieving the so-called singularity, as much of the buzz around them suggests, then the chief difference between them and Frankenstein is one of ability rather than ambition. If eventually they are able to realise their goals and intelligences emerge, full of information, but unnurtured and unloved, how will they behave? Is there any reason to think that they will be more Adam than Satan when we are their creators? 

At the end of Shelley’s novel, an unreconstructed Frankenstein tells his tale to a polar explorer in a ship just coming free from the pack ice. The explorer is facing the choice of plunging onward in the pursuit of knowledge, glory, and, possibly, death, or heeding the call of human connections, his sister’s love, his crew’s desire to see their families. Frankenstein urges him on, appeals to all his ambitions, hoping to drown out the call of home. He fails. The ship turns homeward. Knowledge shorn of attachment, ambition that ignores obligation, these, Shelley tells us, are not worth pursuing. Will we listen to her warning? 

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Article
AI - Artificial Intelligence
Culture
Generosity
Psychology
Virtues
5 min read

AI will never codify the unruly instructions that make us human

The many exceptions to the rules are what make us human.
A desperate man wearing 18th century clothes holds candlesticks
Jean Valjean and the candlesticks, in Les Misérables.

On average, students with surnames beginning in the letters A-E get higher grades than those who come later in the alphabet. Good looking people get more favourable divorce settlements through the courts, and higher payouts for damages. Tall people are more likely to get promoted than their shorter colleagues, and judges give out harsher sentences just before lunch. It is clear that human judgement is problematically biased – sometimes with significant consequences. 

But imagine you were on the receiving end of such treatment, and wanted to appeal your overly harsh sentence, your unfair court settlement or your punitive essay grade: is Artificial Intelligence the answer? Is AI intelligent enough to review the evidence, consider the rules, ignore human vagaries, and issue an impartial, more sophisticated outcome?  

In many cases, the short answer is yes. Conveniently, AI can review 50 CVs, conduct 50 “chatbot” style interviews, and identify which candidates best fit the criteria for promotion. But is the short and convenient answer always what we want? In their recent publication, As If Human: Ethics and Artificial Intelligence, Nigel Shadbolt and Roger Hampson discuss research which shows that, if wrongly condemned to be shot by a military court but given one last appeal, most people would prefer to appeal in person to a human judge than have the facts of their case reviewed by an AI computer. Likewise, terminally ill patients indicate a preference for doctor’s opinions over computer calculations on when to withdraw life sustaining treatment, even though a computer has a higher predictive power to judge when someone’s life might be coming to an end. This preference may seem counterintuitive, but apparently the cold impartiality—and at times, the impenetrability—of machine logic might work for promotions, but fails to satisfy the desire for human dignity when it comes to matters of life and death.  

In addition, Shadbolt and Hampson make the point that AI is actually much less intelligent than many of us tend to think. An AI machine can be instructed to apply certain rules to decision making and can apply those rules even in quite complex situations, but the determination of those rules can only happen in one of two ways: either the rules must be invented or predetermined by whoever programmes the machine, or the rules must be observable to a “Large Language Model” AI when it scrapes the internet to observe common and typical aspects of human behaviour.  

The former option, deciding the rules in advance, is by no means straightforward. Humans abide by a complex web of intersecting ethical codes, often slipping seamlessly between utilitarianism (what achieves the most amount of good for the most amount of people?) virtue ethics (what makes me a good person?) and theological or deontological ideas (what does God or wider society expect me to do?) This complexity, as Shadbolt and Hampson observe, means that: 

“Contemporary intellectual discourse has not even the beginnings of an agreed universal basis for notions of good and evil, or right and wrong.”  

The solution might be option two – to ask AI to do a data scrape of human behaviour and use its superior processing power to determine if there actually is some sort of universal basis to our ethical codes, perhaps one that humanity hasn’t noticed yet. For example, you might instruct a large language model AI to find 1,000,000 instances of a particular pro-social act, such as generous giving, and from that to determine a universal set of rules for what counts as generosity. This is an experiment that has not yet been done, probably because it is unlikely to yield satisfactory results. After all, what is real generosity? Isn’t the truly generous person one who makes a generous gesture even when it is not socially appropriate to do so? The rule of real generosity is that it breaks the rules.  

Generosity is not the only human virtue which defies being codified – mercy falls at exactly the same hurdle. AI can never learn to be merciful, because showing mercy involves breaking a rule without having a different rule or sufficient cause to tell it to do so. Stealing is wrong, this is a rule we almost all learn from childhood. But in the famous opening to Les Misérables, Jean Valjean, a destitute convict, steals some silverware from Bishop Myriel who has provided him with hospitality. Valjean is soon caught by the police and faces a lifetime of imprisonment and forced labour for his crime. Yet the Bishop shows him mercy, falsely informing the police that the silverware was a gift and even adding two further candlesticks to the swag. Stealing is, objectively, still wrong, but the rule is temporarily suspended, or superseded, by the bishop’s wholly unruly act of mercy.   

Teaching his followers one day, Jesus stunned the crowd with a catalogue of unruly instructions. He said, “Give to everyone who asks of you,” and “Love your enemies” and “Do good to those who hate you.” The Gospel writers record that the crowd were amazed, astonished, even panicked! These were rules that challenged many assumptions about the “right” way to live – many of the social and religious “rules” of the day. And Jesus modelled this unruly way of life too – actively healing people on the designated day of rest, dining with social outcasts and having contact with those who had “unclean” illnesses such as leprosy. Overall, the message of Jesus was loud and clear, people matter more than rules.  

AI will never understand this, because to an AI people don’t actually exist, only rules exist. Rules can be programmed in manually or extracted from a data scrape, and one rule can be superseded by another rule, but beyond that a rule can never just be illogically or irrationally broken by a machine. Put more simply, AI can show us in a simplistic way what fairness ought to look like and can protect a judge from being punitive just because they are a bit hungry. There are many positive applications to the use of AI in overcoming humanity’s unconscious and illogical biases. But at the end of the day, only a human can look Jean Valjean in the eye and say, “Here, take these candlesticks too.”   

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Since Spring 2023, our readers have enjoyed over 1,000 articles. All for free. 
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If you enjoy Seen & Unseen, would you consider making a gift towards our work?

Do so by joining Behind The Seen. Alongside other benefits, you’ll receive an extra fortnightly email from me sharing my reading and reflections on the ideas that are shaping our times.

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