management


The relevance paradox


How to make sure your learning filter bubble is the right size Richard Naish T


he term ‘filter bubble’ has been bouncing around the online social networks for nearly a year now; ever since Eli Pariser published his book on the subject and presented on idea-sharing website TED in May 2011. Internet years are like dog years: one year of discussion on the social networks is equivalent to seven years of discussion in the older media of magazines and newspapers. So there must be something in his concept that resonates with many people for it to last so long. But what relevance does it have for learning?


Internet companies like Google and Facebook are


trying to help us deal with the vast amount of information presented on the internet. They do this by using algorithms that personalise our internet experience for us. This is both good and bad. It is good that we see information that is more relevant to our interests, but bad because it means there is a lot of information perhaps we should see, but don’t, because the algorithms edit it out for us. As Mark Zuckerberg, CEO and founder of


Facebook, observed: “A squirrel dying in front of your house may be more relevant to your interests right now than people dying in Africa.” So Facebook is set up to suggest people you might like to be friends with who live near you, know the people you know and are interested in the things that you are interested in. It filters out people who are not friends of your friends, who don’t live nearby and are not interested in what you are interested in. In fact, Pariser noted that if you stop clicking on certain groups of friends on Facebook, the algorithm


Internet years are like dog years: one year of discussion on the social networks is equivalent to seven years of discussion in the older media of magazines and newspapers


‘learns’ that you are not interested in them and will stop showing you their newsfeeds. Google’s search algorithm apparently uses 57


different ‘signals’ to determine what Google search results to show you. Signals such as where you live, what language you speak, what kind of device you are using and what web pages you have previously visited. And the algorithms work even if you are not ‘signed in’ to Google when you are searching. Pariser showed screenshots of two very different friends in different parts of the world who searched for the exact same word and the same time. The search results were very different. But it is not just Facebook and Google who personalise what we see on the internet. Twitter.com shows you people you should follow, who are just like you and tweet about the things you tweet about. LinkedIn.com will suggest people you should contact based on your existing business contacts and groups of which you are part. Also all newspaper apps on your tablet or smartphone allow you to customise/filter your news content, so you only see the news you want to see. So you don’t see the news that, in hindsight, you would have liked to see but the filter you set was too indiscriminate to pick it up for you.


And it is not just algorithms that filter for us, we also filter what we hear about when we select which phone we buy. For example, Blackberry Messenger (BBM), is heavily used by the 11-25 year age group for group chatting on Blackberry phones. If you don’t have a Blackberry you are excluded from those conversations, parties and get-togethers. So with all these filters, there is a danger that we are missing out on information that the algorithms consider irrelevant to us, but may actually turn out to be relevant to us. This is known as the ‘relevance paradox’ and can lead to poor decision-making. It happens in military decisions when some of the intelligence provided to the decision-making HQ is ‘filtered out’ by gatekeepers who do not consider it relevant and so it is never presented to the decision- makers.


In the old media world, those gatekeepers were


… if we just learn more about stuff that is relevant to us, we may miss out on something that is incredibly important, but at first does not appear relevant and so is filtered out


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newspaper editors, publishing companies and governments. In the new media world, it is a combination of everybody online and these filtering algorithms that are the new gatekeepers. When it comes to our learning experience, if we just learn more about stuff that is relevant to us, we may miss out on something that is incredibly important, but at first does not appear relevant and so is filtered out. Perhaps the solution is to try and keep our minds open to learning about stuff that does not initially seem relevant, to connect with people who are different to ourselves and be a liberal gatekeeper.


Richard Naish, independent e-learning consultant


e.learning age april 2012


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