How did you meet your wife? Through an Algorithm
Unpacking How Algorithms are Shaping our World and Culture
Humans are part of the natural system and create social structures including culture and technology. Culture is the balance of beliefs and attitudes, actions and experiences and a sense of purpose; it can create pleasure or pain and can drive action (positive reinforcement) or inaction. Last, culture is cultivated through education, systematic improvement and refinement of the mind. Technology is a driving force that was activated in the 3rd industrial revolution and reactivated since the 4th industrial revolution. The 4th industrial revolution exponentially changed the way we live, work and relate to one another due to technology and innovation that is disrupting almost every industry. Professor Klaus Schwab, founder and executive chairman of the World Economic Forum, described this revolution when he said, “The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril. My concern, however, is that decision-makers are too often caught in traditional, linear (and non-disruptive) thinking or too absorbed by immediate concerns to thank strategically about the forces of disruption and innovation shaping our future (1).”
We are undoubtedly in the age of algorithmic culture and algorithms are shaping our current lives and future.
What is Algorithmic Culture?
According to Merriam-Webster dictionary, Algorithm is a “procedure for solving a mathematical problem in a finite number of steps that frequently involves repetition of an operation (2).” In fact, the definition on the site uses algorithm in an example sentence around the idea of technology stating “there are several search engines, with Google, Yahoo and Bing being the biggest players. Each search engine has its own proprietary computation (called an algorithm) that ranks websites for each keyword or combination of keywords (2).” Algorithms play a massive role in most of the technology humans interact with on a daily basis. In addition to Google, Yahoo and Big, companies such as Tinder (dating), Instagram (social), Nordstrom (retail), and Netflix (entertainment) all collect and track user behavior to glean insights on what to recommend to the consumer next. According to researchers Natasha Just and Michael Latzer, Netflix tracks users’ historical consumption of media in order to predict what the consumer may want to watch next based on themes, genre, actors among other inputs. The mechanism of selection is “categorized into input-throughout-output model and according to the mechanism, results of the algorithmic selection are not the analysis of causes by evidence from abundance historical data (3).”
Benefits and Pitfalls
The majority of content that is consumed on multiple platforms (mobile, connected TV, online, social) through multiple distribution channels and publishers (e.g. from Youtube to Netflix to ABC News) is recommended and predicted by automated and instantaneous algorithmic systems. These algorithms are revolutionizing communications and the way information is digested by humans. If design is in everything, then systems always exist, and the algorithmic system is one that emphasizes data-driven personalization. Personalization is the key benefit of an algorithmic culture. According to mathematician and author of Hello World: Being Human in the Age of Algorithms Hannah Fry, algorithms are “incredibly effective and they’ve made all of our lives a lot easier. Algorithms are incredibly consistent, they never get tired and they’re absolutely precise (4).” How many times have you watched Netflix and you finish a series and the platform serves up a new series for you to binge and it’s a series that you absolutely loved? How many times have you “discovered” a series that you’d never heard about from Australia or the UK ? Netflix uses data formulas to understand the taste, preferences and psychographics of its subscribers in order to recommend content. That’s the benefit of algorithms — it helps people discover content or dress or boyfriend or sneaker or photo that the otherwise would not encounter.
However, one could argue that this same benefit could be viewed as a pitfall. Take for example Netflix; in reality the algorithm only serves up a small portion of content rather than the overall catalog. By only showing a few recommendations, Netflix is layering in a filter, narrowing options for the user, with 75% of users selecting movies based on Netflix’s recommendations (5). The abundance of algorithms is anxiety inducing as every single move is watched (tracking on digital platforms) and it feels like we are not making decisions for ourselves anymore; data and formulas living in a black box are deemed more efficient and less biased decision makers. And while algorithms may control consumer’s behaviors and promise great personalization for online experiences, at the end of the day algorithms do not understand context or nuance. Emotions or empathy cannot be driven by a formula — at least not for now. Algorithms left alone to its own devices can lead to negative decision making; a specific example can be using an algorithm for hiring purposes in human resources. If a company was to choose candidates solely on a predictive model using data from historical recruiting, the algorithm will not layer in emotions, empathy, facial and body language, and may keep the pool of applicants too similar to the current employee base.
At the end of the day, in an algorithmic culture, humans must partner with an algorithm to drive optimal results; one’s benefits or pitfalls does not out-weight the other, at least not for now.
Sources
2) https://www.merriam-webster.com/dictionary/algorithm
3) Just, N., & Latzer, M. (2017). Governance by algorithms: Reality construction by algorithmic selection on the internet. Media, Culture & Society, 39(2), 238–258
4) https://www.vox.com/technology/2018/10/1/17882340/how-algorithms-control-your-life-hannah-fry
6)https://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world
