Attitudes towards Artificial Intelligence
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Predicting what lies ahead is already within the reach of artificial intelligence (AI). Where and when crimes are likely to occur is now mapped by police using such systems, and clinicians draw on them to identify which patients face the highest risk of a heart attack or stroke. Some researchers go further, trying to equip AI with a form of imagination so it can prepare for outcomes nobody has foreseen. Since so many of our choices depend on a reliable forecast, and machines are nearly always better forecasters than people, one might expect these tools to be welcomed. In practice, our confidence in what AI predicts stays strikingly low. Again and again, people prefer the judgement of human specialists to that of a machine, clinging to it even where the specialist proves mistaken. For AI to do any real good, then, a way must be found of persuading people to trust it, which means first working out why they are so unwilling to.
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Consider Auren for Cardiology, one of the flagship systems developed by the technology corporation Torvald Systems. The effort to sell this program to heart specialists turned into a public-relations catastrophe. What the system promised was expert-grade advice on how to treat fifteen cardiac conditions that between them made up roughly 70 percent of the world's cases. Yet the cardiologists who first worked with it were placed in an awkward bind. Whenever Auren's advice matched a doctor's own view, that doctor saw little value in it: the machine was merely confirming what was already known, and nothing about the patient's treatment actually changed. Whenever its advice cut against the specialist's judgement, on the other hand, the usual conclusion was that the system could not be trusted. Nor could the program set out any reason why its proposed treatment made sense, since the machine-learning methods behind it were far too intricate for any person to follow in full. The upshot was still deeper doubt and mistrust, with a great many doctors brushing aside recommendations that struck them as bizarre and falling back on their own expertise.
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This is only one instance of the broader wariness people show towards AI and their hesitation to embrace what it offers. When we trust another person, that trust usually rests on some grasp of how they reason and on past evidence that they are dependable, which leaves us feeling psychologically secure. A machine offers little of this: for most of us AI remains a recent, unfamiliar arrival. Its reasoning can sometimes be explained in technical terms, though not always, yet even then the route by which it reaches a decision lies beyond ordinary comprehension. Dealing with something we cannot make sense of unsettles us and leaves us feeling that command is slipping away. There is a further problem: because AI usually works quietly in the background, few people notice the countless occasions when it performs perfectly well. What they register instead are the times it fails. Those embarrassing lapses draw press coverage out of all proportion to how often they occur, driving home the idea that technology cannot be relied upon, even though machine learning falls short partly because the people who build it are fallible too.
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The emotions AI stirs up run deep. One recent study gathered participants from all sorts of backgrounds, showed each a selection of science-fiction films about AI, then questioned them on the role of automation in daily life. Strikingly, the tone of the film made no difference: whether the on-screen future was hopeful or bleak, the mere act of watching pushed people's views towards the extremes. Those already optimistic grew more fervent, while the doubters became warier than before. This points to a tendency to seize on evidence about AI selectively, bending it to support whatever one already believes — the familiar habit called 'confirmation bias'. Should AI feature ever more prominently in entertainment and the media, the result may be a society divided between those it serves and those who turn away. More to the point, anyone who refuses the benefits AI can bring risks being left at a considerable disadvantage.
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Encouragingly, some remedies are already in view. One is exposure itself: as the earlier experiment showed, people with prior dealings with AI tend to think far more highly of it, and the same appears to hold for other tools — the more someone uses the internet, say, the readier they are to trust it. A second approach is to be more open about the algorithms AI relies on and what they are meant to achieve. Several prominent online marketplaces and social-media firms already publish transparency reports covering government demands and surveillance requests; extending that practice to AI could give the public a clearer sense of how algorithmic decisions are reached.
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There is also research indicating that letting people exercise some say over the decisions AI makes can build trust while giving the system a chance to learn from human experience. In one such study, participants who were permitted to adjust an algorithm even slightly reported greater satisfaction with the choices it produced, a stronger belief that it outperformed the alternatives, and a greater readiness to rely on it again. Grasping the elaborate machinery inside these systems is not the point; what matters is that when people are handed a measure of responsibility for how AI is put to use, they prove far more willing to let it into their lives.
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Question 1 - 6
Choose the correct heading.
List of heading
Public suspicion surrounding a new AI product
Promoting transparency about the way AI reaches its conclusions
How AI guides people through sound decision-making
Why machine-generated predictions surpass those of people
A gap in attitudes that continues to grow wider
An AI tool that worked far better than expected
Why human assessments inspire more confidence than AI ones
The benefits of giving users a role in AI systems