🔄FROM THE ARCHIVE: Deep learning algorithms are prone to a previously unknown problem, say a team of computer scientists at Google.
In recent years, machines have become almost as good as humans, and sometimes better, in a wide range of abilities — for example, object recognition, natural language processing and diagnoses based on medical images.
First, some background. Machine learning involves training a model with data so that it learns to spot or predict features. The Google team picks on the example of training a machine learning system to predict the course of a pandemic.Epidemiologists have built detailed models of the way a disease spreads from infected individuals to susceptible individuals, but not to those who have recovered — and so are immune.
Epidemiologists desperately need to know this early in a pandemic since it determines when and if hospitals will become overwhelmed. The problem arises because the machine learning process has no way to properly choose between these pairs. Indeed, the Google team goes on to show that the parameters the machine chooses can depend on entirely arbitrary decisions in the way the model is set up.
The Google team shows that small changes, such as modifying the random seeds used in training, can force a model towards an entirely different solution and so lead to different predictions. They also show how this can cause the models to inherit biases in the data set that have nothing to do with prediction task they are undertaking.
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