Bias in Technology

While researching on algorithmic bias I found a very insightful TED Talk by Eli Pariser about Filter Bubbles. Pariser says that Filter Bubbles are “your own personal, unique universe of information that you live in online. And what's in your filter bubble depends on who you are, and it depends on what you do. But the thing is that you don't decide what gets in. And more importantly, you don't actually see what gets edited out.” (Pariser, 2011). He describes in his talk how algorithms define what gets filtered in or filtered out of our information feeds. Beware online "filter bubbles" (source: TED Talks) A good example of this filtering is what we experience in social media. Algorithms decide what to show and what not to show. This decision making seems to be neutral, but as I have discussed in previous posts, no algorithm is neutral given that they are only mathematical representations of a set of particular human views of what should be prioritized and what not. ...

     While researching on biased algorithm's examples I was not able to find simple ones that would not require comprehensive in-deep knowledge of other topics like coding or statistics, so I thought it would be fun to make and analyze a very simple algorithm for bias. The goal is to understand how bias works in an algorithm.      I've picked the cities of Lewiston (ID) and Clarkston (WA) for this example. These two cities share the Lewis-Clark valley at the confluence of the Snake and the Clearwater rivers. Now, let's assume we work for a real state investment firm looking into building new housing developments. Let's assume another algorithm had already segmented the real state market in the LC valley in four sections based on population and other parameters: Red, Yellow, Blue, and Green. Let's assume we are tasked with creating an algorithm that would decide where next the company should invest. The four parameters we are going to use for our very simple...

As a Computer Science Major, I spend most of my days learning and perfecting the skill required to communicate with zeros and ones with fascinating non-sentient systems and embed logic into their memories, so they follow my commands. This sorcery is usually referred to as coding! Just like with magic, we can also program these systems to consume large quantities of data and spit back countless secrets hidden in it; or perhaps answer complex questions that would take us (humans) sometimes up to several lifetimes to compile. This magic is the result of algorithms defined in code. Well-designed code-embedded data-based decision-making logics had been the attainable holy grail for many organizations in recent times and some of us,  data sorcerers,  had been happily non-stop coding as many decisions in our lives as caffeine allows.   We often debate on the quality of the code and the algorithms, but rarely talk about the source data. Even more rarely do we discuss the quality...