AI & Machine Learning

(Sriram, Danely, Alvin, Xeon) Artificial Intelligence and Machine Learning are more and more prevalent in every aspect of our lives, from policing to banking. As such, it is important that these algorithms are designed in ethical ways. Unfortunately, in the banking sector, that is not always the case. These algorithms have advantages including increasing speed, reducing mistakes, reducing labor costs, and enhancing customer experience. However, algorithms are not without bias. Bias can come from unrepresentative or biased data samples used to train the model, bias in the training of the model, or bias in the original design or adaptation after contact with new data. This is especially bad when credit scoring company uses shopping patterns or social media activity, which could be incorrect, in a non-transparent process, and result in denying credit without the consumer knowing why. Furthermore, an algorithm could use factors such as race or writing style, which have no dire...