Any summary of the consequences of algorithms on modern society will be an understatement as it’s difficult to fully communicate the impact of such a wide-spread concept in just a few paragraphs. Much of the modern world is now governed by algorithms: anything you can imagine doing on the internet or your smartphone is mostly an algorithmic process. The majority of trades in financial markets are performed by algorithms, which means that much of your savings and pension is now managed by lines of code as much as human decision-making. It’s not just financial decisions that are being outsourced to algorithms but many decisions in medical care, public policy, and business management are now a product of some form of AI.
Algorithms are the foundation of Artificial Intelligence (AI). Many AI programs today rest on a machine learning algorithm. These algorithms seek patterns in vast data sets and are superior to traditional algorithms. Imagine you were writing a program that would identify photos of tigers. You would have to explicitly incorporate the necessary logic that would distinguish a photograph with a tiger. If you think of this in the form of a flowchart, you might see binary questions like whether the animal has the color orange in its coat. The problem with this method, however, is that tigers might be more complicated than you thought. There are too many rules that need to be programmed, and without them, the program would be prone to lots of mistakes, such as the inability to identify a white tiger. A machine learning algorithm, however, would be shown thousands of pictures of tigers and non-tigers and would make a prediction on whether a photo was of a tiger or not based on the statistical regularities it observed. It can then refine its process based on the feedback it receives following its predictions.
This process is omnipresent in the digital world. Machine learning algorithms on the internet seek patterns in data, incorporating the inputs, such as user behavior, in order to create some output, such as a curated news feed. Machine learning algorithms on Netflix, for example, will make predictions as to what content you might want to watch, based on what you’ve watched in the past, and make recommendations based on its predictions. The algorithm will further refine its predictions as you accept or reject its recommendations. Note how this is different from a predetermined set of criteria outlined by a programmer. This process of learning allows the algorithms to improve with more data, a fact that is largely responsible for the growth of the big data economy.
When many people think of AI, they think of human-like robots that are coming to take over the world. What’s escapes our attention is all of the ways that AI is already embedded in our daily lives. Most people now carry an abundance of machine learning algorithms in their pockets. There is no doubt that algorithms – sometimes synonymous with “technology” – have made our lives easier in many ways. Instead of having to consult an ambiguous city map, we can let Google Maps tell us exactly where to go, and instead of parsing through countless texts in a public library to find a simple fact, we can retrieve it in milliseconds through a search engine. Given the power and ubiquity of such a concept, it is no surprise that algorithms carry tremendous social responsibility and are subject to widespread public discussion.