Explainer
AI
Belief
Creed
5 min read

Whether it's AI or us, it's OK to be ignorant

Our search for answers begins by recognising that we don’t have them.

Simon Walters is Curate at Holy Trinity Huddersfield.

A street sticker displays multiple lines reading 'and then?'
Stephen Harlan on Unsplash.

When was the last time you admitted you didn’t know something? I don’t say it as much as I ought to. I’ve certainly felt the consequences of admitting ignorance – of being ridiculed for being entirely unaware of a pop culture reference, of being found out that I wasn’t paying as close attention to what my partner was saying as she expected. In a hyper-connected age when the wealth of human knowledge is at our fingertips, ignorance can hardly be viewed as a virtue. 

A recent study on the development of artificial intelligence holds out more hope for the value of admitting our ignorance than we might have previously imagined. Despite wide-spread hype and fearmongering about the perils of AI, our current models are in many ways developed in similar ways to how an animal is trained. An AI system such as ChatGPT might have access to unimaginable amounts of information, but it requires training by humans on what information is valuable or not, whether it has appropriately understood the request it has received, and whether its answer is correct. The idea is that human feedback helps the AI to hone its model through positive feedback for correct answers, and negative feedback for incorrect answers, so that it keeps whatever method led to positive feedback and changes whatever method led to negative feedback. It really isn’t that far away from how animals are trained. 

However, a problem has emerged. AI systems have become adept at giving coherent and convincing sounding answers that are entirely incorrect. How has this happened? 

This is a tool; it is good at some tasks, and less good at others. And, like all tools, it does not have an intrinsic morality. 

In digging into the training method for AI, the researchers found that the humans training the AI flagged answers of “I don’t know” as unsatisfactory. On one level this makes sense. The whole purpose of these systems is to provide answers, after all. But rather than causing the AI to return and rethink its data, it instead developed increasingly convincing answers that were not true whatsoever, to the point where the human supervisors didn’t flag sufficiently convincing answers as wrong because they themselves didn’t realise that they were wrong. The result is that “the more difficult the question and the more advanced model you use, the more likely you are to get well-packaged, plausible nonsense as your answer.” 

Uncovering some of what is going on in AI systems dispels both the fervent hype that artificial intelligence might be our saviour, and the deep fear that it might be our societal downfall. This is a tool; it is good at some tasks, and less good at others. And, like all tools, it does not have an intrinsic morality. Whether it is used for good or ill depends on the approach of the humans that use it. 

But this study also uncovers our strained relationship with ignorance. Problems arise in the answers given by systems like ChatGPT because a convincing answer is valued more than admitting ignorance, even if the convincing answer is not at all correct. Because the AI has been trained to avoid admitting it doesn’t know something, all of its answers are less reliable, even the ones that are actually correct.  

This is not a problem limited to artificial intelligence. I had a friend who seemed incapable of admitting that he didn’t know something, and whenever he was corrected by someone else, he would make it sound like his first answer was actually the correct one, rather than whatever he had said. I don’t know how aware he was that he did this, but the result was that I didn’t particularly trust whatever he said to be correct. Paradoxically, had he admitted his ignorance more readily, I would have believed him to be less ignorant. 

It is strange that admitting ignorance is so avoided. After all, it is in many ways our default state. No one faults a baby or a child for not knowing things. If anything, we expect ignorance to be a fuel for curiosity. Our search for answers begins in the recognition that we don’t have them. And in an age where approximately 500 hours of video is uploaded to YouTube every minute, the sum of what we don’t know must by necessity be vastly greater than all that we do know. What any one of us can know is only a small fraction of all there is to know. 

Crucially, admitting we do not know everything is not the same as saying that we do not know anything

One of the gifts of Christian theology is an ability to recognize what it is that makes us human. One of these things is the fact that any created thing is, by definition, limited. God alone is the only one who can be described by the ‘omnis’. He is omnipotent, omnipresent, and omniscient. There is no limit to his power, and presence, and knowledge. The distinction between creator and creation means that created things have limits to their power, presence, and knowledge. We cannot do whatever we want. We cannot be everywhere at the same time. And we cannot know everything there is to be known.  

Projecting infinite knowledge is essentially claiming to be God. Admitting our ignorance is therefore merely recognizing our nature as created beings, acknowledging to one another that we are not God and therefore cannot know everything. But, crucially, admitting we do not know everything is not the same as saying that we do not know anything. Our God-given nature is one of discovery and learning. I sometimes like to imagine God’s delight in our discovery of some previously unknown facet of his creation, as he gets to share with us in all that he has made. Perhaps what really matters is what we do with our ignorance. Will we simply remain satisfied not to know, or will it turn us outwards to delight in the new things that lie behind every corner? 

For the developers of ChatGPT and the like, there is also a reminder here that we ought not to expect AI to take on the attributes of God. AI used well in the hands of humans may yet do extraordinary things for us, but it will not truly be able to do anything, be everywhere, or know everything. Perhaps if it was trained to say ‘I don’t know’ a little more, we might all learn a little more about the nature of the world God has made. 

Article
AI
Attention
Culture
5 min read

Will AI’s attentions amplify or suffocate us?

Keeping attention on the right things has always been a problem.

Mark is a research mathematician who writes on ethics, human identity and the nature of intelligence.

A cute-looking robot with big eyes stares up at the viewer.
Robots - always cuter than AI.
Alex Knight on Unsplash.

Taking inspiration from human attention has made AI vastly more powerful. Can this focus our minds on why attention really matters? 

Artificial intelligence has been developing at a dizzying rate. Chatbots like ChatGPT and Copilot can automate everyday tasks and can effortlessly summarise information. Photorealistic images and videos can be generated from a couple of words and medical AI promises to revolutionise both drug discovery and healthcare. The technology (or at least the hype around it) gives an impression of boundless acceleration. 

So far, 2025 has been the year AI has become a real big-ticket political item. The new Trump administration has promised half a trillion dollars for AI infrastructure and UK prime minister Keir Starmer plans to ‘turbocharge’ AI in the UK. Predictions of our future with this new technology range from doom-laden apocalypse to techno-utopian superabundance. The only certainty is that it will lead to dramatic personal and social change. 

This technological impact feels even more dramatic given the relative simplicity of its components. Huge volumes of text, image and videos are converted into vast arrays of numbers. These grids are then pushed through repeated processes of addition, multiplication and comparison. As more data is fed into this process, the numbers (or weights) in the system are updated and the AI ‘learns’ from the data. With enough data, meaningful relationships between words are internalised and the model becomes capable of generating useful answers to questions. 

So why have these algorithms become so much more powerful over the past few years? One major driver has been to take inspiration from human attention. An ‘attention mechanism’ allows very distant parts of texts or images to be associated together. This means that when processing a passage of conversation in a novel, the system is able to take cues on the mood of the characters from earlier in the chapter. This ability to attend to the broader context of the text has allowed the success of the current wave of ‘large language models’ or ‘generative AI’. In fact, these models with the technical name ‘Transformer’ were developed by removing other features and concentrating only on the attention mechanisms. This was first published in the memorably named ‘Attention is All You Need’ paper written by scientists working at Google in 2017. 

If you’re wondering whether this machine replication of human attention has much to do with the real thing, you might be right to be sceptical. That said, this attention-imitating technology has profound effects on how we attend to the world. On the one hand, it has shown the ability to focus and amplify our attention, but on the other, to distract and suffocate it. 

Attention is a moral act, directed towards care for others.

A radiologist acts with professional care for her patients. Armed with a lifetime of knowledge and expertise, she diligently checks scans for evidence of malignant tumours. Using new AI tools can amplify her expertise and attention. These can automatically detect suspicious patterns in the image including very fine detail that a human eye could miss. These additional pairs of eyes can free her professional attention to other aspects of the scan or other aspects of the job. 

Meanwhile, a government acts with obligations to keep its spending down. It decides to automate welfare claim handling using a “state of the art” AI system. The system flags more claimants as being overpaid than the human employees used to. The politicians and senior bureaucrats congratulate themselves on the system’s efficiency and they resolve to extend it to other types of payments. Meanwhile, hundreds of thousands are being forced to pay non-existent debts. With echoes of the British Post Office Horizon Scandal, the 2017-2020 the Australian Robo-debt scandal was due to flaws in the algorithm used to calculate the debts. To have a properly functioning welfare safety net, there needs to be public scrutiny, and a misplaced deference to machines and algorithms suffocated the attention that was needed.   

These examples illustrate the interplay between AI and our attention, but they also show that human attention has a broader meaning than just being the efficient channelling of information. In both cases, attention is a moral act, directed towards care for others. There are many other ways algorithms interact with our attention – how social media is optimised to keep us scrolling, how chatbots are being touted as a solution to loneliness among the elderly, but also how translation apps help break language barriers. 

Algorithms are not the first thing to get in the way of our attention, and keeping our attention on the right things has always been a problem. One of the best stories about attention and noticing other people is Jesus’ parable of the Good Samaritan. A man lies badly beaten on the side of the road after a robbery. Several respectable people walk past without attending to the man. A stranger stops. His people and the injured man’s people are bitter enemies. Despite this, he generously attends to the wounded stranger. He risks the danger of stopping – perhaps the injured man will attack him? He then tends the man’s wounds and uses his money to pay for an indefinite stay in a hotel. 

This is the true model of attention. Risky, loving “noticing” which is action as much as intellect. A model of attention better than even the best neuroscientist or programmer could come up with, one modelled by God himself. In this story, the stranger, the Good Samaritan, is Jesus, and we all sit wounded and in need of attention. 

But not only this, we are born to imitate the Good Samaritan’s attention to others. Just as we can receive God’s love, we can also attend to the needs of others. This mirrors our relationship to artificial intelligence, just as our AI toys are conduits of our attention, we can be conduits of God’s perfect loving attention. This is what our attention is really for, and if we remember this while being prudent about the dangers of technology, then we might succeed in elevating our attention-inspired tools to make AI an amplifier of real attention. 

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