test

This is just a test post

Comments

Post a Comment

Popular posts from this blog

Algorithmic Bias: Is Perfectly Imperfect Good Enough?

Image
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...
Read more

Algorithmic Bias and Filter Bubbles

Image
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. ...
Read more

An Insightful and Inspiring Tech Talk by Cathy O'Neil on Algorithmic Bias

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...
Read more