Eliminating Bias in Machine Learning: Gold In, Garbage Out
Data scientists have long been aware of the concept of “garbage in, garbage out” — the idea that the quality of results is a direct indicator of the quality of data. Indeed, much effort has been expended in the pursuit of cleansing data to ensure its accuracy. It then should come as no surprise that AI and machine learning (ML) algorithms are also subject to the same quality standards.