Bias in Technology

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 minimum, we m

Wondering how smart assistants like Siri or Alexa keep getting better at understanding voice instructions over time, or how Netflix seems to keep getting better at suggesting content? Or perhaps you wonder sometimes how you are getting all these ads about something you never searched for but was exactly what you needed? These are only some of the most visible and least critical interactions we have with Artificial-Intelligence-developed algorithms every day of our lives. They are designed to learn from us, analyze our interests and decision-making thinking, and ultimately outsmart us. The Wall Street Journal has a very interesting article describing how the use of AI is expanding: “We are witnessing a turning point for artificial intelligence, as more of it comes down from the clouds and into our smartphones and automobiles (…) Shield AI, a contractor for the Department of Defense, has put a great deal of AI into quadcopter-style drones which have already carried out—and continue to be

    As science and technology advancements continue pushing the boundaries of what computational power can do, digital data keeps growing in popularity as the preferred method to store and search for human knowledge. Recently, Machine Learning approaches to processing this vast and growing universe of datasets have started to be used to improve efficiency and to be able to keep up with the speed at which our digital universe is growing, not to mention the growing digital cloud empowered by high-speed data transmission. Machine Learning has opened the door to autonomous algorithm-based decision-making. These technologies are the building blocks of Artificial Intelligence.      Basic Artificial Intelligence technology is currently being used to support or even completely overtake decision-making in multiple industry sectors and human ingenuity may likely one day bring into reality more autonomous versions of Artificial Intelligence. Worrall in a 2015 National Geographic article already w

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 of that data.