Algorithms: biased or objective?
Journalism has traditionally idealized objectivity as one of it’s core ethical principles for delivery of news reports. Journalists have aimed to provide citizens with impartial, unbiased news whereby they promised to “stick to the facts” and avoid imposing any interpretation or opinionated perspectives on any given issue (Ward, 2008, p.73). The ideal of objectivity served journalists as it furnished them with a set of clear guidelines, standards, and attitudes that disciplined their field such as neutrality, non-interpretation, and non-bias (Ward, 2008, pp.73-74). Journalism has aimed to factually document reality in its “bareness”.
Nonetheless, journalism became obsessed with objectivity that it no longer regarded it as a mean to attain “the truth” but rather as an end in itself. “The doctrine was so pervasive that in 1956 press theorist Theodore Peterson said that objectivity was “a Fetish” (Peterson, 1956 p.88, as cited by Ward, 2008, p.75). This obsession with objectivity rendered news reporting a mere listing of un-interpreted facts overlooking other essential journalistic duties “such as commenting, campaigning, and acting as a public watchdog (Ward, 2008 p.75).” The question arises: What is the functionality of any fact in itself when we strip it off its context? Aren’t citizens better off when they are offered a wide variety of competing interpretations out of which they can formulate their own opinions? Here, Hegel’s dialectic clearly illustrates this as it proposes that opposing contradictory sides don’t nullify one another but on the contrary unite to bring us closer to the “truth”. “The back-and-forth debate between opposing sides produces a kind of linear progression or evolution in philosophical views or positions (Stanford Encyclopedia of Philosophy, 2016).”
However, today the circumstances have drastically changed. With the age of information and the rise of social media, Ward (2008) argues that Journalism has abandoned “traditional objectivity” towards an interpretive opinionated form of journalism with citizens and bloggers as reporters. Social media, i.e Facebook and Twitter, have offered everyone equal access to global platforms through which they can freely express their opinions with minimal to no restriction. From the ethical obligation to subdue one’s opinion journalism has shifted to the other end: an uncensored expression of opinion. For instance, right now, I can login to twitter and tweet about anything I want, instantly reaching a wide audience; no fact check, no proof reading, no gatekeeping, something that was impossible only two decades ago. Accordingly, Ward’s (2008) suggests that “citizen reporting” has allowed for the “democratization of news media” where everyone can unrestrictedly express their opinions (p.76).” This way everyone attains access to a market of competing viewpoints enabling individuals to make judgments for themselves which is exactly what a democracy requires.
Only that the reality of the new media is far more complicated when we start to think about their mechanism, namely algorithms. Algorithms are “encoded procedures for transforming input data into a desired output, based on specified calculations (Gillepsie, 2012, p.1).” When I logon to Facebook right now and scroll through my newsfeed, an algorithm has already made the decision for me regarding what order the posts appear in. It ranks posts based on a set formula that takes into account a number of factors besides recency including mutual friends, popularity of the user, frequency of interaction with the user and so on (Gillespie, 2014). In other words, Facebook’s algorithm makes editorial decisions for me arranging the posts in the order that it considers will best satisfy my interests. As Gillespie (2014) puts it, Facebook is “carefully crafted to be an engaging flow of material.” While it is true that Facebook aims to best serve us proving us with news that corresponds to our interests and beliefs, it is nonetheless subduing other news that might be of great benefit to us. Consider a user on your newsfeed who is not prioritized by the algorithm. This user makes a statement that clashes with your own beliefs. Would you rather be exposed to that alternative opinion or remain sheltered in your own bubble of happiness? This speaks to Gillespie’s argument, that Facebook is responsible for a hazardous confusion between commodity and the public’s need for information (2014).
Now let’s consider Twitter’s algorithm for deciding what the trending topic is. On September 15, 2011, the Occupy Wall Street, a pro-democracy people-powered groundbreaking movement in history of US politics didn’t make it to the list of Twitter’s trending topics in New York. While many speculated that censorship was involved, the reasons turned out to be purely algorithmic. #occupy wall street was competing against #what you should know about me, #I cant respect you if, and Kim Kardashian’s wedding (Lotan, 2013). Accordingly, The voices of thousands of protestors were subdued as twitter’s algorithm prioritized news about a reality TV celebrity’s marriage.
According to Lotan (2013), The new media have brought about a new dichotomy concerning news: what gets published vs. what gets attention. The internet is oversaturated with information, and this is why we need algorithms navigate our way through the web. Nonetheless, while we might think that algorithms which function solely on quantitate mathematical basis are the way to solve the problem of human bias in news reporting, the reality is far away from that: algorithmic bias is real as we’ve seen in the previous examples; algorithms are accumulating tremendous power and we still don’t fully understand how they function. In other words, they are tremendously involved in moulding our culture when for instance they decide on our behalf what the trendiest topic on Twitter is. The question is: Are we at a point where we should be implementing algorithmic law or regulation? Perhaps, issuing a law that balances algorithmic intelligence with human intelligence is our best option. For instance, Twitter ought to dedicate a team of humans to monitor algorithmic calculation on the basis of a code of Newsworthiness whereby issues of national significance and urgencies are always given attention regardless of algorithmic calculations. Maybe this way Kim Kardashian’s wedding wouldn’t stand in the way of social change.
Gillespie, T. (2014). Facebook’s algorithm — why our assumptions are wrong, and our concerns are right. Culture Digitally. Retrieved from http://culturedigitally.org/2014/07/facebooks-algorithm-why-our-assumptions-are-wrong-and-our-concerns-are-right/
Gillespie, T. (2012). The releveance of algorithms. Media Technologies, Boczkowski, P. and Foot, K (eds.), Cambridge, MA: MIT Press
Lotan, G. (2013.) Networked audiences. McBride, K. & Rosenstiel, T. (eds.), The new Ethics of Journalism , Sage, London
Maybee, Julie E., “Hegel’s Dialectics”, The Stanford Encyclopedia of Philosophy (Winter 2016 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/win2016/entries/hegel-dialectics/>.
Ward, S. (2009). Truth and objectivity in Wilkins & Christians (eds.) Handbook of Mass Media Ethics, Routledge, London, New York