“Big Data” and machine learning are used in a wide variety of disciplines, from making credit and insurance decisions to driving medical research, but how accurate is this approach? If algorithms are ground-truthed used a biased population, the results of those algorithms will also be biased. Alex Lancaster has a great post on his blog at Biosystems Analytics about the potential consequences of using biased training data.
It’s often widely assumed that decisions made by algorithms are more “neutral” and “fair” than those made by humans….machine learning algorithms, specifically “classifier” systems, trained on statistically dominant populations, can sometimes lead to erroneous classifications.
read more at Biosystems Analytics