Facebook’s mission is to “Give people the power to build community and bring the world closer together” . To achieve its mission and to connect more than 2 billion people around the world, Facebook employs machine learning algorithms to drive all aspects of its user experience, from ranking posts for News Feed and deciding what ads to show to which users, to classifying photos and videos. ,
In particular, Facebook learns about its user interests and preferences by analyzing how they interact with content, other users, pages and businesses on the platform. It then ranks the content on the News Feed and it generates revenues by allowing advertisers to target users with the most relevant ads in an auction-based system. To do so, Facebook needs to make sense of large quantities of data, which are mostly unstructured. This is where deep learning (a subfield of machine learning) comes into play, with techniques that allow data to self-classify. Textual analysis, facial recognition and granularly-targeted advertising are all examples of how deep learning supports Facebook’s mission. 
In 2018, there are 1.5 billion users logging into the platform each day. When Facebook was launched in 2004, most of the content was in the form of text. With the introduction of images, audio, videos and other rich media such as 360 degree photos and videos, analyzing all these data has become an increasingly complex task. The large quantity of data, its nature and its complexity require Facebook not only to master machine learning and artificial intelligence as internal core capabilities today but also to heavily invest to advance the research on the field in the longer term. At Facebook, the Facebook Artificial Intelligence Research group (FAIR) led by Yann LeCun, neural network expert, is tasked with advancing the longer-term academic problems surrounding AI, while the Applied Machine Learning group (AML), led by Joaquin Quiñonero Candela, is charged with integrating the research into Facebook’s products. As Mark Zuckerberg once put it, “One of our goals for the next five to 10 years, is to basically get better than human level at all of the primary human senses: vision, hearing, language, general cognition.” ,
But is Facebook really getting better than humans or is it carrying and amplifying all of our biases?
Nowadays social media platforms such as Facebook have become one of the primary sources of news and information. A survey conducted by Pew Research Center surfaced that as of 2017, 67% of Americans get at least some of their news on social media, up an absolute 5% from the year before and largely driven by increases among the older and less educated segments of the population. Specifically, 45% of Americans consume news on Facebook. 
Moreover, the way social media allows users to interact with content has led these platforms to become the new “public sphere”: a place where people freely discuss key social and political issues. Facebook, however, is a company and not a public space where all content has equal importance. With the intent of providing relevant content and ads to its users as well as maximizing their engagement, Facebook deep learning algorithms dictate what each user is allowed to see by serving billions of personalized News Feeds. More specifically, Facebook developed a recommender system called “Collaborative Filtering”, through which it serves content based on the preferences of like-minded people with similar tastes and socio-demographic background. With regards to news, this means that each single user is more likely to see the content that best aligns to her or his own views. This, exacerbated by the psychological tendency of human beings to search and interpret information in a way that confirms pre-existing beliefs (known as “confirmation bias”), effectively turns Facebook into the largest “echo chamber” in the world. ,
The way Facebook employs machine learning algorithms promotes a narrow view of the world to its users. And when the plurality of opinions as a backbone of democracy ceases to exist, the society becomes more polarized to extreme points of view, making it harder to reach a common agreement.
Finally, when a for-profit company, and not a regulated news organization, becomes a primary channel to distribute third party information (with little or no oversight), the possibility of abuse is almost endless. As a matter of fact, recent events have shown how social media, especially Facebook, might have played a role in key political events such as the US presidential elections, by expanding the reach of fake news. 
In the future, should Facebook take the responsibility of detecting and eliminating fake news, as well as that of providing the opportunity for its users to be exposed to dissenting views of the world, effectively promoting democracy? Can artificial intelligence help? If so, how?
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 Guess, Andrew. Nyahn, Brendan. Reifler, Jason. 2018. “Selective Exposure to Misinformation: Evidence from the consumption of fake news during the 2016 U.S. presidential campaign”. http://www.dartmouth.edu/~nyhan/fake-news-2016.pdf
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