AI and Bias | 3rd | week 3 |.
So we hear about concerns and issues with AI. By us being an example, can you talk a little bit about that? So to start off with the bias problem, this is a very famous problem within the field of machine learning. To give an example, let's go back to the Microsoft Surface facial recognition. So Microsoft Surface facial recognition is great system that works very nicely. But there's a little problem. It has a very difficult time understanding and recognizing the faces of people in certain demographics. The reason for this is because well, there simply wasn't enough representative data of those people's faces in the training dataset that Microsoft used. This is really something that's very difficult to get around. We certainly have to be cognizant with AI systems of things like systematic bias and ethical issues related to AI. We need to be sure that the data that we feed to our AI systems does not have or does not contain bias. Or that we are able to adjust for that bias, so that we make sure we're not misrepresenting the population as a whole or preferring certain groups over others. I think this is still a very challenging issue that we'll need to work through as these AI systems are developed. First of all, machine learning technology is inherently biased. Machine learning works off of the fundamental assumption of bias. That's how machine learning technology functions. It's biasing certain input data points to map them to other output data points.