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The Pros and Cons of Using Algorithms

Modern life without the magic of algorithms is impossible to imagine. These clever mathematical tools help us sort information and navigate the internet with greater ease than ever before. But algorithms aren’t flawless. As with all things, there are pros and cons to their use. Let’s take a look at some of these below.

Pro: Algorithms Learn What We Like

One of the greatest assets of algorithms is that they learn from our behavior, interests or activity and use this information in order to make predictions about what we’d like to see. If you’re a fan of K-drama, for example, the algorithm employed by Netflix will quickly identify this and begin to recommend new shows in this genre to you. This aids discovery by removing the work you would have had to do to find new shows that match this criteria.

Algorithms work best when they recommend things to us we didn’t even know we wanted to see. On social media, for example, algorithms use what’s known as a social graph to identify what other people with similar interests already enjoy, and surface this content on our own feeds.

Con: Algorithms Cannot Match the Trustworthiness of Experts

At the end of the day, an algorithm is little more than a sophisticated tool used to sort information. That makes it extremely useful in a wide range of situations, but when we’re wanting to be absolutely certain that we can trust the advice and recommendations we’re receiving, algorithms perform poorly in surveys when compared with the advice of human experts.

For this reason, when the stakes are low – for example, when we’re looking to find a new comedy movie to watch, this fact means little. But when people are looking to part with their money, there are many situations where consumers – given the choice, would prefer human input.

For example, in the world of real money online gaming, players want to be absolutely sure that their decision on which platform or bonus to choose can be relied upon. For this reason, industry mainstays like CasinoReviews have distinguished themselves amidst the fray of algorithmic recommendation engines due to their commitment to providing comprehensive reviews of gaming platforms, all conducted by industry experts.

Likewise, when people are in the market to purchase contents or automotive insurance, most would be happier to make their decision based on the bespoke recommendations of other humans with the vital know-how to guide them to a decision they can rely upon, rather than having to resort to making the decision based on an algorithmic toplist.

Pro: Algorithms Can Handle Vast Data-Sets

The reason algorithms have emerged as a prominent part of modern life is that there is simply too much information online today for humans to sort, understand and navigate. Algorithms help us rapidly filter through vast quantities of data, enabling us to connect with the information, content or media we want without having to put in the legwork ourselves.

Algorithms Can Handle Vast Data-Sets

Consider shopping online, for example. Amazon alone currently offers in excess of 12 million products to purchase. If this quantity were available in a physical store, the floorplan would be larger than 8 football fields and incredibly difficult to navigate. With an algorithm, however, we can instantaneously be directed to the products we’re looking for.

Con: Algorithms Can Trap You Within Information Silos

Another key drawback of algorithms is that their capacity to recommend and iterate based on your activity can give rise to a phenomenon known as information silos. This is when an algorithm effectively predicts what you’re looking for to such an extent that you can no longer access information that does not conform with your preferences. 

Now this is fine when we’re talking about what we want to see on our Instagram feed, but it becomes an issue when we’re attempting to gain contrasting data points, alternative views or randomized suggestions. In such cases, an algorithm can effectively filter out information that doesn’t match our biases or interests – resulting in our ability to assess things objectively being hampered.

Say you only listen to classical music on Spotify. In such a situation, this software will recommend to you an endless stream of Bach, Beethoven and Mozart. But suppose one day you decided you wanted to listen to 90s pop hits. 

The algorithm would not have predicted this to be an option, so would not recommend you the likes of The Spice Girls or Britney Spears. In such a situation, you would have to, yourself, seek out this music based upon researching relevant artists.