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Algorithmic Bias: Is Perfectly Imperfect Good Enough?

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A Forbes article titled “Perfectly Imperfect: Coping With The ‘Flaws’ Of Artificial Intelligence (AI)” claims that AI will never be perfect and that bias should be expected and accepted by society. That implicit bias, poor data, and people expectations make the case for AI to never be perfect. It goes beyond that and claims that AI is perfectly imperfect (Sahota, 2020). Nevertheless, the article’s points of view have one common flaw, the unparalleled size and speed at which AI can operate. As mentioned in an article published by McKinsey and Company on bias in AI: “extensive evidence suggests that AI models can embed human and societal biases and deploy them at scale” (Silberg & Manyika, 2020). XKCD (AI Hiring Algorithm, n.d.) makes a great example of this in Figure 1.   Figure 1 . AI Hiring Algorithm. (Source: xkcd.com,  n.d.)   Although it is logical to understand that no creation can be perfect and that all we can do is to limit bias and other issues with AI to its...
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Algorithmic Bias and Filter Bubbles

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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. ...
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A Very Simple Bias Algorithm Using Real Data

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     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...
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