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In his article, “Hacking AI: Rethinking cybersecurity for artificial intelligence” [5], Davey Gibian explores how traditional cybersecurity is insufficient for evolving AI technologies. He also states that what is needed for cybersecurity is “two algorithm‐level considerations: robustness and explainability” [5]. One interesting point that he goes on to make under “robustness” is talking about eliminating bias as part of AI cybersecurity. In this report, we will examine how this bias can be caused by ethics implemented into AI.

The concept of traditional cybersecurity insufficient for modern and future AI technology is also supported by Ilja Moisejevs in his article “What everyone forgets about machine learning” [6]. Here, he briefly goes through the history of cybersecurity and cyber threats. He then goes on to explain the need for cybersecurity in ML and the impact that failure to implement this can cause (ssss1).

If one were to go through a history of literature, that person would find out that humans have often fantasized about the creation of non‐human acting, responding, and thinking as if they were human. Many stories that fall in the genre of science fiction often depict the use of robots functioning using AI either for good, or for bad. Although the fantasy of AI stretches back quite far, the modern age of AI begins around the era of the 1950s [7]. The difference between this era versus previous written versions of the future of AI stems from the fact that Alan Turing had begun development of code that would yield the first AI machines, turning what was once science fiction into reality.