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Search Technologies have been using big data processing and machine learning to improve search results for a while now, but it is only recently that we have come to understand that these same techniques can be used for personalization. To efficiently search huge volumes of data, these search engines use diverse smart techniques and algorithms to deliver fairly accurate user interest-based content.Google, for example, is known to keep data of the past searches, device usage, location, demographics and more of users to predict and provide quick and easy access to interest-based results. The availability of this vast data is why we can search for ‘McDonald’s’’ and get the five locations closest to us, instead of the ones in faraway China.In offering personalized content and experiences geared toward users’ individual interests, machine learning algorithms are injected into almost every platform to predict user’s intentions based on what the platforms have learned from the user’s behavioral and historical data, and these recommender systems are assumed to present to the user tailored information while reducing news diversity, thus leading to partial information blindness (i.e., filter bubbles).Wikipedia describes the filter bubble as a state of intellectual isolation which can result from personalized searches when a website algorithm selectively guesses what information a user would like to see, based on the information the relevant search engine has picked up about the user. In order words, a user’s filter bubble is his personal, unique universe of information which the user has online, and what’s in his filter bubble depends on who the user is, where he lives, what he does and what sites he visits.These filter bubbles limits users from harnessing the full potential of the internet, contrary to the original intention - which is the unrestricted access to a vast amount of information. An idea which prevailed in the last 10-15 years, but is fast declining in today’s world.Today’s Internet giants — Google, Facebook, Yahoo and Microsoft, and other technology companies see the remarkable rise of available information as an opportunity. If they can provide services that sift through the data and supply us with the most personally relevant and appealing results, they’ll get the most users and the most ad views. As a result, they’re racing to offer personalized filters which show users the internet they think we want to see. These filters, in effect, control and limit the information that reaches our screens.By now, everyone is familiar with sponsored ads which follow us around online, based on our recent clicks on commercial websites, this is as a result of the increasing and nearly invisible storage of our personal information, with and sometimes without our consent. For instance two users who each individually search on Google for “Nigeria” may get significantly different results, based on their past clicks; aggregators like Yahoo News and Google News make adjustments to their home pages for each individual visitor, and this technology is beginning to make inroads on the websites of newspapers like The Washington Post and The New York Times.As aptly put by Facebook’s CEO, Mark Zuckerberg while emphasizing the importance of news feed in Facebook and how they need to be customized from user to user: “A squirrel dying in front of your house may be more relevant to your interests right now than people dying in Africa” – one of the probable baselines for Facebook tailored news feed. At Facebook, “relevance” is virtually the sole criterion that determines what users see. Focusing on the most personally relevant news — the squirrel — is a great business strategy, but it leaves us staring at our front yard instead of reading about suffering, genocide, and revolution.In a 2010 interview with the Wall Street Journal, former CEO of Google, Eric Schmidt said, “It will be very hard for people to watch or consume something that has not in some sense been tailored for them” referencing the power of individual targeting.All of this is fairly harmless when information about consumer products is filtered into and out of our personal universe. But when personalization affects not just what we buy but how we think, different issues arise. We get trapped in a filter bubble and are not exposed to information that could challenge or broaden our worldview. Globalization and democracy depends on the citizen’s ability to engage with multiple viewpoints; the internet limits such engagement when it offers up only information that reflects only our already established point of view. While it’s sometimes convenient to see only what we want to see, it’s critical at other times that we see things that we don’t.What we are witnessing currently is the passing of the torch; from human gatekeepers to algorithms. The algorithms do not possess the ethics of human editors, hence, if algorithms are going to curate the world for us by deciding what we see and what we don’t see, then we need to make sure they are not just keyed to relevance, but that they also show us the full picture. They need to show us not just information (based on previously gathered data), or Kim K’s take on an issue (because it’s generally sought after), but also what’s relevant to our search in faraway Timbuktu.In today’s world of growing data, personalization is indispensable for efficient search and relevancy. It has succeeded in creating the structured web content and unequivocally brought significant benefits to the users which are of great value provided the user in question consents, failing which it not only limits but also constitutes a breach of relevant regulations such as the General Data Protection Regulation which went into effect in May 2018.We are living in an exciting, innovative and slightly scary world. I believe the majority of the benefits of personalized search driven by machine learning and pattern matching tend to outweigh the risks, but we must be ever-diligent to ensure we don't develop a permanent privacy and perspective blind spot.References:“Beware online "filter bubbles" – Eli Pariser: TED Talk” – (2010)
“The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think” – Eli Pariser - 2011
“Filter Bubble” – Wikipedia“When the Internet think It Knows You” – NY Times – (2011, May 23)“Google chief warns on social networking dangers” – The Guardian – (2010)“How can recommender systems incorporate diversity and break filter bubbles?” – Quora – (2016)
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