The Influence of Algorithm-Mediated Learning on Social Perceptions

Category Technology

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People’s interactions with online algorithms mediate how they learn from others, and can have potentially dangerous consequences including social misperceptions, conflict, and spread of misinformation. Algorithms are designed to amplify PRIME information that humans naturally tend to learn from; however, when this information is selectively amplified and misused the results can be greater political conflict and more misinformation. Though research is still in its infancy, research does suggest that algorithm-mediated social learning can lead to false polarization.

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People’s daily interactions with online algorithms affect how they learn from others, with negative consequences including social misperceptions, conflict and the spread of misinformation, my colleagues and I have found. People are increasingly interacting with others in social media environments where algorithms control the flow of social information they see. Algorithms determine in part which messages, which people and which ideas social media users see. On social media platforms, algorithms are mainly designed to amplify information that sustains engagement, meaning they keep people clicking on content and coming back to the platforms. I’m a social psychologist, and my colleagues and I have found evidence suggesting that a side effect of this design is that algorithms amplify information people are strongly biased to learn from. We call this information "PRIME," for prestigious, in-group, moral and emotional information.

Humans naturally have cognitive biases that cause them to pay more attention to PRIME information than to other types of information when learning from social media algorithms

In our evolutionary past, biases to learn from PRIME information were very advantageous: Learning from prestigious individuals is efficient because these people are successful and their behavior can be copied. Paying attention to people who violate moral norms is important because sanctioning them helps the community maintain cooperation. But what happens when PRIME information becomes amplified by algorithms and some people exploit algorithm amplification to promote themselves? Prestige becomes a poor signal of success because people can fake prestige on social media. Newsfeeds become oversaturated with negative and moral information so that there is conflict rather than cooperation.

A recent study shows that misinformation is more likely to spread when amplified by algorithms due to PRIME information included in posts which make posts more engaging and shareable

The interaction of human psychology and algorithm amplification leads to dysfunction because social learning supports cooperation and problem-solving, but social media algorithms are designed to increase engagement. We call this mismatch functional misalignment.

Why it matters .

One of the key outcomes of functional misalignment in algorithm-mediated social learning is that people start to form incorrect perceptions of their social world. For example, recent research suggests that when algorithms selectively amplify more extreme political views, people begin to think that their political in-group and out-group are more sharply divided than they really are. Such "false polarization" might be an important source of greater political conflict.

False polarization caused by algorithm-mediated learning can lead to increased political conflict and risk of violence

Functional misalignment can also lead to greater spread of misinformation. A recent study suggests that people who are spreading political misinformation leverage moral and emotional information – for example, posts that provoke moral outrage – in order to get people to share it more. When algorithms amplify moral and emotional information, misinformation gets included in the amplification.

What other research is being done .

Recent surveys demonstrate that opinions are divided on the impact of algorithm-mediated learning on offline political polarization

In general, research on this topic is in its infancy, but there are new studies emerging that examine key components of algorithm-mediated social learning. Some studies have demonstrated that social media algorithms clearly amplify PRIME information. Whether this amplification leads to offline polarization is hotly contested at the moment. A recent survey of experts found that people disagree about whether social media can lead to offline polarization.

Algorithms increase engagement on social media platforms by selectively amplifying information which caters to users’ interests

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