Building Algorithms with our Online Data

The Ted Talk from this week was eye opening to say the least.  I always had an understanding of how companies use algorithms to lead us to certain pages or to target ads more specifically to us, however, some of the examples that were used by Zeynep Tufekci.  She mentions early on the science of “persuasion architecture.”  The example that she used was candy and gum in super markets at the eye level of children so that they pester their parents to the point of submission and the parents have virtually no choice but to placate them and purchase the items.  The way it is explained in the video, websites such as facebook and youtube collect information, using things you may have searched, and even things you began to search, deleted, never hit enter to formally search.  They even collect data that was never even truly data.  As someone who enjoys trips to Atlantic City too much, the example she used about enticing people to get to Las Vegas is frightening.  The thought that companies were using this algorithms to prey on people who may, by what their pages are telling us, are suffering from some kind of manic state or in a bipolar episode.  Not to get the person help, but simply to take advantage and advertise some sort of mechanism to convince this person to make a trip to vegas.  It begs the question of “where does it end?”  The reality of the matter is it won’t.  These kinds of algorithms and data collection tactics will only continue to dive deeper to find ways to monetize and mobilize our technology in ways that profits all of the companies that use these different interfaces.

I want to take a minute to talk about Facebook dark posts.  The example that was used was that facebook was making posts targeted at African-American males to dissuade them from voting.  Dark posts meaning that only the people that they meant for people to see them will see them.  While it may have been the Trump campaign that made this plan in motion, on some level, Facebook is responsible for going along with it.  Examples such as this are dangerous tales of how our technology and our information could be used, packaged, and sold for others to do whatever they want with it.  Moving into another example, she talked about youtube videos and how they collect data to show videos that “you might be interested in.”  What struck me most about this was when she began talking about how the algorithm factored in what you might be interested in, as well as what other people who have viewed this video might be interested in.  To me, this is a golden example of people’s information moving freely on the invisible marketplace that is the internet.  This all begs the question of, if we are aware to some capacity that they are using our information to these ends, what don’t we know about?  If our information ends up on lists designed for business to solicit services to us, how else is that same information working behind the scenes without us even knowing?  All scary thoughts.  Look forward to Sun finding paths for us to explore this further and excited to hear everyones takes on this. 

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