Unsupervised learning, such as what we see happening in the autoencoder we have been exploring so far, is not magical. It is well established and has very rigorous boundaries that are known and pre-defined. It does not have the capability of learning new things outside the limitations given by the data. Remember, unsupervised learning is passive learning as explained in the introductory section of this chapter.
However, even the most robust of unsupervised learning models have ethical risks associated with them. One of the major problems is that they create difficulties when dealing with outliers or data that may contain edge cases. For example, say that there is a large amount of data for IT recruitment, which includes years of experience, current salary, and programming languages that a candidate knows. If the data mostly contains data about candidates with the same programming language experience, and only a few know Python, then those...
