Predictive analytics often combines different machine learning and statistical techniques; one model might score how likely a group of customers is to churn, with another model predicting which channel you should use to contact each person with an offer that might keep them as a customer.
Navigating the downsides of machine learning
Because machine learning systems aren't explicitly programmed to solve problems, it’s difficult to know how a system arrived at its results. This is known as a “black box” problem, and it can have consequences, especially in regulated industries.
As machine learning becomes more widely used, you’ll need to explain why your machine learning-powered systems do what they do. Some markets — housing, financial decisions and healthcare — already have regulations requiring you to give explanations for decisions. You may also want algorithmic transparency so that you can audit machine learning performance. Details of the training data and the algorithms in use isn’t enough. There are many layers of non-linear processing going on inside a deep network, making it very difficult to understand why a deep network is making a particular decision. A common technique is to use another machine learning system to describe the behavior of the first.
You also need to be aware of the dangers of algorithmic bias, such as when a machine learning system reinforces the bias in a data set that associates men with sports and women with domestic tasks because all its examples of sporting activities have pictures of men and all the people pictured in kitchens are women. Or when a system that correlates non-medical information makes decisions that disadvantage people with a particular medical condition.
Machine learning can only be as good as the data it trains on to build its model and the data it processes, so it’s important to scrutinize the data you’re using. Machine learning also doesn't understand the data or the concepts behind it the way a person might. For example, researchers can create pictures that look like random static but get recognized as specific objects.
There are plenty of recognition and classification problems that machine learning can solve more quickly and efficiently than humans, but for the foreseeable future machine learning is best thought of as a set of tools to support people at work rather than replace them.
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