I'm confused about the difference between "ethics" and "bias" when those concepts are discussed in the context of Machine Learning (ML). In my understanding, ethical issue in ML is pretty much exactly the same thing as "bias": say, the model discriminates people of color and this is the same as to say that the model is biased. In short, "ethics is always a bias, but it is not necessarily true that a bias is always an ethical issue". Is this …

Most advanced supervised learning techniques are non-deterministic by construction. The final output of the model usually depends on some random parts of the learning process. (Random weight initialization for Neural Networks or variable selection / splits for Gradient Boosted Trees). This phenomenon can be observed by plotting the predictions for a given random seed against the predictions for another seed : the prediction are usually correlated but don't coincide exactly. Generally speaking it is often not a problem. When trying …

In our organization, we are currently in the phase of building up team, skills to automate and implement AI based solutions. So, we are very early in this AI journey. Right now, we are also working on identifying some of the problems that we face in our business. For example, when we get 8 customer segments, but only 2 of them bring in a lot of revenue. Rest all of them perform poorly. We would like to find out why …