Try and wrap your mind around the idea of an "unbiased" AI. It is not possible. As a trainer of an AI, simply looking at a picture and saying that it's a cat,

Try and wrap your mind around the idea of an "unbiased" AI. It is not possible. As a trainer of an AI, simply looking at a picture and saying that it's a cat, and then telling the AI that it's a cat, is biased. You are automatically assuming you are correctly identifying it as a cat, and teaching the mind of an AI to follow your presumption. The only way to make an AI less biased (but still biased), is to diversify your labeled training data. And by diversify, you need to diversify the presumptions on the labels of the data themselves. What is the confidence among several people that this picture is of a cat? Rather than one trainer. This is a simplistic example. If you scale it to more complicated tasks, such as understanding natural language, body language, intent of users, or anything related to ethics, you'll likely end up with a crazy AI. And yes, I mean crazy. As in, simultaneously holding very seemingly opposing understandings of reality and spitting out data that reflects those oppositions. If you've ever had more than one authoritative figure in your life give you contradicting advice to another - one parent holding differing beliefs to another, or one teacher to another in school, you may notice yourself starting to qualify your teacher's competence as a factor in the fidelity of their teachings. A scary, but simultaneously cool thought: An AI that questions my capacity to teach it, while I'm teaching it. Bias will always remain an element in the equation however. One AI will likely have interacted with different trainers or data, and create contradicting understandings of reality to that of another AI.