Filtering
The last algorithmic decision I’ll consider here is filtering, which involves including or excluding information according to various rules or criteria. Indeed, inputs to filtering algorithms often take prioritizing, classification, or association decisions into account. In news personalization apps like Zite or Flipboard news is filtered in and out according to how that news has been categorized, associated to the person’s interests, and prioritized for that person.
Filtering decisions exert their power by either over-emphasizing or censoring certain information. The thesis of Eli Pariser’s16 is largely predicated on the idea that by only exposing people to information that they already agree with (by overemphasizing it), it amplifies biases and hampers people’s development of diverse and healthy perspectives. Furthermore, there’s the issue of censorship. Weibo, the Chinese equivalent to Twitter, uses computer systems that constantly scan, read, and censor any objectionable content before it’s published. If the algorithm isn’t sure, a human censor is notified to take a look.17