Rise Of The Machines: How AI & Algorithms Control What You Watch | Features | LIVING LIFE FEARLESS

Rise Of The Machines: How AI & Algorithms Control What You Watch

In the recent years, there’s been a frightening thing about the internet that has kept me up at night thinking of how it’s possible for it to know important things in relation to who I am or what I want even before I do. Artificial intelligence has become so advanced and widespread to the point that most companies are adopting the technology and integrating it into our everyday lives.

As streaming platforms became an integral and leading source of entertainment for us, we are given the opportunity to access nearly endless amounts of content ranging from films, TV shows, music, podcasts, etc. in the comfort of our home. Although, having numerous options actually does more harm than good when it comes to decision-making.

We are given the opportunity to access nearly endless amounts of content

Oftentimes, we become paralyzed from browsing endlessly and sifting through content only to end up losing interest and putting the remote down. Thus, streaming giants in the entertainment industry invested in artificial intelligence to build an algorithm that will make audiences’ lives easier by suggesting films or series based on their preferences and past viewing experience. Let’s get into the basics of how algorithms and recommendation systems work, their implications, and their direct effects on the filmmakers and creators behind them.

Decoding the System

In simple terms, algorithms are defined as “a set of step-by-step procedures, or a set of rules to follow, for completing a specific task or solving a particular problem.” It uses machine learning and artificial intelligence in analyzing statistics based on a particular study or data.

Streaming platform algorithms can be organized into three different categories:

  1. A content-based algorithm provides similar recommendations based on a product or content that the user has seen or interacted with in the past.
  2. The collaborative Filtering algorithm gets data from similar interests and behaviors of users instead of the actual product or content.
  3. A knowledge-based algorithm gives recommendations based on the query or information provided by the user, then the system goes through the media library to find similar content.

It’s common among streaming platforms such as Netflix, Amazon, and Hulu to use a combination of the abovementioned algorithms, which take into account data such as user ratings, click, genre, age, gender, and location to better personalize the recommendations.

“Take Squid Game – it might well be that the way to have a large launch is partly to do with the algorithmic promotion of widely watched content. Its success is an example of how algorithms can reinforce what is already popular,” via The Conversation.

Negative Impacts of Algorithms

While algorithms are designed to provide solutions that will greatly benefit users, we can’t overlook the fact that algorithms can also be problematic for some films or series that are isolated by the system. This is very limiting and biased for similar content that deserves to be seen but is simply being pushed out of the algorithm, which also doesn’t leave room for discovering new content or categories that might be as interesting. Also, there are instances when you unintentionally watch a film that you end up not liking and this entirely changes the algorithm that you worked hard to curate.

What does that do to the art of cinema?

– Martin Scorsese

Not to mention, films that do not get into the algorithm could be detrimental to its filmmakers, especially emerging creatives who do not have the same popularity or exposure that is recognized by the system. In fact, Martin Scorsese criticized how streaming platforms are not properly curating films and TV series at a level that gives it justice. “If further viewing is ‘suggested’ by algorithms based on what you’ve already seen, and the suggestions are based only on subject matter or genre, then what does that do to the art of cinema?” He believes that by adopting this system, cinema is “devalued, sidelined, demeaned, and reduced to its lowest common denominator, ‘content.’”

Room for Improvement

As part of the binge generation, personalized algorithms seem to be the answer to our content-related decision-making conundrum as it’s convenient, useful, and fairly straightforward. While it may look good on the surface, there is still a lot of work to be done to address the limitations, biases, and reliability of recommended content. We have yet to figure out how algorithms can ensure that it is not filtering or excluding content from foreign countries, diverse filmmakers, or independent cinema. We also have a duty as a viewer to do our own research and not entirely depend on machines to decide for us every time.

Streaming platforms have definitely revolutionized the way we consume entertainment, but that also means that they have a huge responsibility to design a system that aims to serve its audiences as well as the creatives who tirelessly work to bring these remarkable stories to life.

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