Controlling machine-learning algorithms and their biases

Myths aside, artificial intelligence is as prone to bias as the human kind. The good news is that the biases in algorithms can also be diagnosed and treated. Companies are moving quickly to apply machine learning to business decision making. New programs are constantly being launched, setting complex algorithms to work on large, frequently refreshed data sets. The speed at which this is taking place attests to the attractiveness of the technology, but the lack of experience creates real risks. Algorithmic bias is one of the biggest risks because it compromises the very purpose of machine learning. This often-overlooked defect can trigger costly errors and, left unchecked, can pull projects and organizations in entirely wrong directions.