Facebook pioneered the Newsfeed, the world’s first social algorithm, in 2006 to deal with the abundance of shared content. The algorithm continues to be the secret sauce of its business, a magic formula that it can tweak at any time to favor branded content or individuals, with ads sprinkled in between.
From business to professional sports, algorithms have since gone mainstream. Scouts use statistics as well as behavioral data to determine their team’s future draft picks. Similarly, employers use personality tests to disqualify a majority of their applicants, particularly those with mental illness. According to data scientist and author Cathy O’Neil:
“In the US, some 72% of CVs are never seen by human eyes.”
Algorithms have become a modern way of sifting between mass populations and mass consumption — hello Spotify and Amazon recommendations. What algorithms miss, are the plurality of outliers. People and tastes are exceptional.
Before World War II, the US Army standardized the size of its cockpits to match the average American male’s stature. To recruit more pilots, the Air Force built more flexible seats and adjustable pedals. Diversity won out to demand.
The age of algorithms signals a similar narrow-minded path with potentially disastrous consequences. The concept of automation as a service saves time fails when it comes to sorting out humans. O’Neil continues:
“Their popularity relies on the notion they are objective, but the algorithms that power the data economy are based on choices made by fallible human beings. And, while some of them were made with good intentions, the algorithms encode human prejudice, misunderstanding, and bias into automatic systems that increasingly manage our lives. Like gods, these mathematical models are opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists.”
The machines are us: The tendency to exclude the unpopular, the misfit, the unfit just because it fails to meet a generic formula poses a risk to the potential outlier which disrupts the probabilistic agenda.