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Here’s Why Information Overload May Not be Good

The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms

Information overload may not always be a good thing. Researchers have found that in certain circumstances, having more background information may actually lead people to take worse decisions.

The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms and how healthcare professionals and financial advisors present this new information to their patients and clients.

“Being accurate is not enough for information to be useful,” said Samantha Kleinberg, Associate Professor of Computer Science at Stevens Institute of Technology in New Jersey, US.”It’s assumed that AI and Machine Learning will uncover great information, we’ll give it to people and they’ll make good decisions. However, the basic point of the paper is that there is a step missing: we need to help people build upon what they already know and understand how they will use the new information,” Kleinberg added.

For example, when doctors communicate information to patients, such as recommending blood pressure medication or explaining risk factors for diabetes, people may be thinking about the cost of medication or alternative ways to reach the same goal.

“So, if you don’t understand all these other beliefs, it’s really hard to treat them in an effective way,” said Kleinberg. For the study, the researchers asked 4,000 participants a series of questions about topics with which they would have varying degrees of familiarity.

Some participants were asked to make decisions on scenarios they could not possibly be familiar with. Other participants were asked about more familiar topics i.e. choosing how to reduce risk in a retirement portfolio or deciding between specific meals and activities to manage bodyweight.

The team compared whether people did better or worse with new information or were just using what they already knew. The researchers found that prior knowledge got in the way of choosing the best outcome. Kleinberg found the same to be true when she posed a problem about health and exercise, as it relates to diabetes.

When people without diabetes read the problem, they treated the new information at face value, believed it and used it successfully. People with diabetes, however, started second-guessing what they knew and as in the previous example, did much worse. “In situations where people do not have background knowledge, they become more confident with the new information and make better decisions,” said Kleinberg.

AI
The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms and how healthcare professionals and financial advisors present this new information to their patients and clients. Pixabay

“So there’s a big difference in how we interpret the information we are given and how it affects our decision making when it relates to things we already know vs. when it’s in a new or unfamiliar setting,” she added.

Kleinberg cautioned that the point of the paper is not that information is bad. She argued only that in order to help people make better decisions, it is important to better understand what people already know and tailor information based on that mental model.

ALSO READ: Here’s Why Surgery Would be an Effective Way To Overcome Obesity at an Early Age

Started in 1870, Stevens Institute of Technology is one of the oldest technological institutes in the US. (IANS)

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