Recommender systems are extremely useful at finding online content which is relevant and engaging to us, but they can have a negative effect on our opinions and world view if they are relied upon indiscriminately. As Zeynep Tufekci (2018) explained, we all have natural tendencies to engage with extreme and sensational content. Recommender systems can exploit this tendency, generating perverse feedback loops which lead the system to display increasingly more sensational and extreme content. If people are aware of how recommender systems work, they can enjoy their benefits while being relatively immune to this insidious effect. While stakeholders can take different actions to reduce this problem they all center around education an awareness.

Aside from understanding how recommender systems work, users should also be aware of the range of content and publishers that a particular outlet can provide. While a user may reinforce their liberal views by reading a major liberal newspaper, the content will be vetted to a high standard. Despite there being a political slant, the authors will be professional journalists held a consistent set of norms. On the other hand, if a person is watching YouTube, nearly anyone can publish, and videos from reputable sources can be recommended next to those of private individual peddling conspiratorial content. So, users must be aware that the content they are seeing is being engineering as they browse it, and there can be huge differences in credibility and objectivity between sources, even if they both seem equally well produced.

Companies have always tailored their offerings and marketing to maximize profits, so I don’t think they should be tasked with policing recommendation systems. Instead, companies should do more to make people aware that their content is being modified and the effect this can have on their online experience. Perhaps as users we should be able to interact directly with the recommender systems (DiResta, 2018), correcting it when it starts to wander into extreme territory.

Governments shouldn’t have a direct role in censoring and deciding which content is shown online, so in any case regulation of recommender systems should focus on mandatory warning like those instituted with GPRS which explicitly tell people that their content is being changed. This could be paired with a public campaign to educate people on the possible ramifications.

It’s fair to conclude that most of the radicalizing effects of recommender systems are neutralized when the audience is aware of how these systems work. If people take an active mindset when using them, they can enjoy their benefits without inadvertently drifting into extremism.

References

DiResta, R. (2018) Up Next: A Better Recommendation System, Wired. Available at: https://www.wired.com/story/creating-ethical-recommendation-engines/ (Accessed: 5 July 2019).

Tufekci, Z. (2018) ‘YouTube, the Great Radicalizer’, The New York Times, 11 March, p. SR6. Available at: https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html (Accessed: 5 July 2019).

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