The study

With AI, there’s plenty of advice available, but we don’t make the most of it

According to research carried out by the Universities of Milan-Bicocca and Pavia, easy access to a vast amount of information does not help people make better decisions

3' min read

Translated by AI
Versione italiana

3' min read

Translated by AI
Versione italiana

Thanks to digital tools and AI, we have access to a wealth of high-quality advice, but the ease of access to this advice does not guarantee better decisions. In most professional and everyday contexts, the final decision still rests with a person, who must determine when to trust the adviser, when to rely on their own judgement, and how to combine the two sources of information. This aspect of our decision-making process is explored in the study ‘Adaptive yet suboptimal integration of advice in decision-making’, published in the journal *Communications Psychology* (part of the Nature group) and led by the University of Milan-Bicocca in collaboration with the University of Pavia.

The double experiment

The research team, comprising Joshua Zonca (psychology), Alice Giampino (statistics) and Carlo Reverberi (psychology) from Milano-Bicocca, together with Paolo Cherubini (behavioural sciences, University of Pavia), sought to measure the extent to which people put external advice into practice.

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In the two experiments, 89 participants interacted with seven ‘artificial advisers’, each with a different profile. Some advisers were more or less competent than the participant, some were more overconfident or more cautious, and some were more or less reliable in their confidence in their answers. One final adviser, however, was a sort of ‘lookalike’, with characteristics similar to those of the participant.

The results show that people do not take sufficient account of the characteristics of the advisers. Performance improves after receiving advice, but not as much as it could.

The results

Joshua Zonca explains: ‘To understand how this works, we can use an analogy. Let’s imagine we’ve saved a small amount of money and want to invest it. We already have our own idea, but, to avoid making mistakes, we ask two acquaintances for advice. The first is the classic braggart, always absolutely sure of himself, who tells us without hesitation: ‘Do this!’ The second is more cautious, but we know he’s often right: ‘I think it’s better to do this other thing.’ To make the best decision, we shouldn’t simply listen to whoever speaks with the most conviction: we should consider how competent each person really is and how reliable their confidence is. And, in some cases, we should even give the advice we receive more weight than our initial opinion. Yet this often doesn’t happen.”

Possible causes

The study identifies two main causes. The first is an egocentric bias: decision-makers always tend to place too much weight on their own judgement, whilst underestimating that of the adviser. The second is the difficulty in moving from the general to the specific – that is, in transforming general knowledge about the quality of the source into concrete adjustments in individual decisions: it is as if, when faced with the opinion of an adviser who is more experienced than ourselves, we tell ourselves a little too often, ‘Yes, on average they know more than I do, but not in this particular case’. These difficulties persist even when people are explicitly informed of their own abilities and those of the adviser.

The gap between actual performance and optimal performance was found to be particularly wide among the highest-quality advisers, precisely in those cases where their advice could offer the greatest benefit.

“When designing AI-based decision-support systems, it is not enough simply to provide accurate and transparent advice: we must also train people to use them responsibly and help them understand, decision by decision, how much weight to give to each piece of advice,” concludes Carlo Reverberi.

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